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\n  \n 2024\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n fastMI: A fast and consistent copula-based nonparametric estimator of mutual information.\n \n \n \n \n\n\n \n Purkayastha, S.; and Song, P. X. -.\n\n\n \n\n\n\n Journal of Multivariate Analysis, 201: 105270. May 2024.\n \n\n\n\n
\n\n\n\n \n \n \"fastMI:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{purkayastha_fastmi_2024,\n\tseries = {Copula {Modeling} from {Abe} {Sklar} to the present day},\n\ttitle = {{fastMI}: {A} fast and consistent copula-based nonparametric estimator of mutual information},\n\tvolume = {201},\n\tcopyright = {All rights reserved},\n\tissn = {0047-259X},\n\tshorttitle = {{fastMI}},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0047259X23001161},\n\tdoi = {10.1016/j.jmva.2023.105270},\n\tabstract = {As a fundamental concept in information theory, mutual information (MI) has been commonly applied to quantify association between random vectors. Most existing nonparametric estimators of MI have unstable statistical performance since they involve parameter tuning. We develop a consistent and powerful estimator, called fastMI, that does not incur any parameter tuning. Based on a copula formulation, fastMI estimates MI by leveraging Fast Fourier transform-based estimation of the underlying density. Extensive simulation studies reveal that fastMI outperforms state-of-the-art estimators with improved estimation accuracy and reduced run time for large data sets. fastMI provides a powerful test for independence that exhibits satisfactory type I error control. Anticipating that it will be a powerful tool in estimating mutual information in a broad range of data, we develop an R package fastMI for broader dissemination.},\n\turldate = {2024-03-07},\n\tjournal = {Journal of Multivariate Analysis},\n\tauthor = {Purkayastha, Soumik and Song, Peter X. -K.},\n\tmonth = may,\n\tyear = {2024},\n\tkeywords = {Kernel density estimation, Copula, Fast Fourier transformation, Permutation test, Statistical dependence},\n\tpages = {105270},\n}\n\n
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\n As a fundamental concept in information theory, mutual information (MI) has been commonly applied to quantify association between random vectors. Most existing nonparametric estimators of MI have unstable statistical performance since they involve parameter tuning. We develop a consistent and powerful estimator, called fastMI, that does not incur any parameter tuning. Based on a copula formulation, fastMI estimates MI by leveraging Fast Fourier transform-based estimation of the underlying density. Extensive simulation studies reveal that fastMI outperforms state-of-the-art estimators with improved estimation accuracy and reduced run time for large data sets. fastMI provides a powerful test for independence that exhibits satisfactory type I error control. Anticipating that it will be a powerful tool in estimating mutual information in a broad range of data, we develop an R package fastMI for broader dissemination.\n
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\n \n\n \n \n \n \n \n \n Quantification and cross-fitting inference of asymmetric relations under generative exposure mapping models.\n \n \n \n \n\n\n \n Purkayastha, S.; and Song, P. X. -.\n\n\n \n\n\n\n . 2024.\n Publisher: arXiv\n\n\n\n
\n\n\n\n \n \n \"QuantificationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 19 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{purkayastha_quantification_2024,\n\ttitle = {Quantification and cross-fitting inference of asymmetric relations under generative exposure mapping models},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://arxiv.org/abs/2311.04696},\n\tdoi = {10.48550/ARXIV.2311.04696},\n\tabstract = {In many practical studies, learning directionality between a pair of variables is of great interest while notoriously hard when their underlying relation is nonlinear. This paper presents a method that examines asymmetry in exposure-outcome pairs when a priori assumptions about their relative ordering are unavailable. Our approach utilizes a framework of generative exposure mapping (GEM) to study asymmetric relations in continuous exposure-outcome pairs, through which we can capture distributional asymmetries with no prefixed variable ordering. We propose a coefficient of asymmetry to quantify relational asymmetry using Shannon's entropy analytics as well as statistical estimation and inference for such an estimand of directionality. Large-sample theoretical guarantees are established for cross-fitting inference techniques. The proposed methodology is extended to allow both measured confounders and contamination in outcome measurements, which is extensively evaluated through extensive simulation studies and real data applications.},\n\turldate = {2024-03-21},\n\tauthor = {Purkayastha, Soumik and Song, Peter X. -K.},\n\tyear = {2024},\n\tnote = {Publisher: arXiv},\n\tkeywords = {FOS: Computer and information sciences, FOS: Mathematics, Methodology (stat.ME), Statistics Theory (math.ST)},\n}\n\n
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\n In many practical studies, learning directionality between a pair of variables is of great interest while notoriously hard when their underlying relation is nonlinear. This paper presents a method that examines asymmetry in exposure-outcome pairs when a priori assumptions about their relative ordering are unavailable. Our approach utilizes a framework of generative exposure mapping (GEM) to study asymmetric relations in continuous exposure-outcome pairs, through which we can capture distributional asymmetries with no prefixed variable ordering. We propose a coefficient of asymmetry to quantify relational asymmetry using Shannon's entropy analytics as well as statistical estimation and inference for such an estimand of directionality. Large-sample theoretical guarantees are established for cross-fitting inference techniques. The proposed methodology is extended to allow both measured confounders and contamination in outcome measurements, which is extensively evaluated through extensive simulation studies and real data applications.\n
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\n \n\n \n \n \n \n \n \n Asymmetric predictability in causal discovery: an information theoretic approach.\n \n \n \n \n\n\n \n Purkayastha, S.; and Song, P. X. K.\n\n\n \n\n\n\n November 2023.\n arXiv:2210.14455 [math, stat]\n\n\n\n
\n\n\n\n \n \n \"AsymmetricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@misc{purkayastha_asymmetric_2023,\n\ttitle = {Asymmetric predictability in causal discovery: an information theoretic approach},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tshorttitle = {Asymmetric predictability in causal discovery},\n\turl = {http://arxiv.org/abs/2210.14455},\n\tdoi = {10.48550/arXiv.2210.14455},\n\tabstract = {Causal investigations in observational studies pose a great challenge in scientific research where randomized trials or intervention-based studies are not feasible. Leveraging Shannon's seminal work on information theory, we develop a causal discovery framework of "predictive asymmetry" for bivariate \\$(X, Y)\\$. Predictive asymmetry is a central concept in information geometric causal inference; it enables assessment of whether \\$X\\$ is a stronger predictor of \\$Y\\$ or vice-versa. We propose a new metric called the Asymmetric Mutual Information (\\$AMI\\$) and establish its key statistical properties. The \\$AMI\\$ is not only able to detect complex non-linear association patterns in bivariate data, but also is able to detect and quantify predictive asymmetry. Our proposed methodology relies on scalable non-parametric density estimation using fast Fourier transformation. The resulting estimation method is manyfold faster than the classical bandwidth-based density estimation, while maintaining comparable mean integrated squared error rates. We investigate key asymptotic properties of the \\$AMI\\$ methodology; a new data-splitting technique is developed to make statistical inference on predictive asymmetry using the \\$AMI\\$. We illustrate the performance of the \\$AMI\\$ methodology through simulation studies as well as multiple real data examples.},\n\turldate = {2023-04-27},\n\tpublisher = {arXiv},\n\tauthor = {Purkayastha, Soumik and Song, Peter X. K.},\n\tmonth = nov,\n\tyear = {2023},\n\tnote = {arXiv:2210.14455 [math, stat]},\n\tkeywords = {Statistics - Methodology, Statistics - Computation, Mathematics - Statistics Theory},\n\tfile = {wnar_2023.pdf:/Users/soumikp/Zotero/storage/A7JDQ68R/wnar_2023.pdf:application/pdf},\n}\n\n
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\n Causal investigations in observational studies pose a great challenge in scientific research where randomized trials or intervention-based studies are not feasible. Leveraging Shannon's seminal work on information theory, we develop a causal discovery framework of \"predictive asymmetry\" for bivariate $(X, Y)$. Predictive asymmetry is a central concept in information geometric causal inference; it enables assessment of whether $X$ is a stronger predictor of $Y$ or vice-versa. We propose a new metric called the Asymmetric Mutual Information ($AMI$) and establish its key statistical properties. The $AMI$ is not only able to detect complex non-linear association patterns in bivariate data, but also is able to detect and quantify predictive asymmetry. Our proposed methodology relies on scalable non-parametric density estimation using fast Fourier transformation. The resulting estimation method is manyfold faster than the classical bandwidth-based density estimation, while maintaining comparable mean integrated squared error rates. We investigate key asymptotic properties of the $AMI$ methodology; a new data-splitting technique is developed to make statistical inference on predictive asymmetry using the $AMI$. We illustrate the performance of the $AMI$ methodology through simulation studies as well as multiple real data examples.\n
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\n \n\n \n \n \n \n \n \n Generative causality: using Shannon's information theory to infer underlying asymmetry in causal relations.\n \n \n \n \n\n\n \n Purkayastha, S.; and Song, P. X.\n\n\n \n\n\n\n November 2023.\n arXiv:2311.04696 null\n\n\n\n
\n\n\n\n \n \n \"GenerativePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@misc{purkayastha_generative_2023,\n\ttitle = {Generative causality: using {Shannon}'s information theory to infer underlying asymmetry in causal relations},\n\tcopyright = {All rights reserved},\n\tshorttitle = {Generative causality},\n\turl = {http://arxiv.org/abs/2311.04696},\n\tabstract = {Causal investigations in observational studies pose a great challenge in scientific research where randomized trials or intervention-based studies are not feasible. Leveraging Shannon's seminal work on information theory, we consider a framework of asymmetry where any causal link between putative cause and effect must be explained through a mechanism governing the cause as well as a generative process yielding an effect of the cause. Under weak assumptions, this framework enables the assessment of whether X is a stronger predictor of Y or vice-versa. Under stronger identifiability assumptions our framework is able to distinguish between cause and effect using observational data. We establish key statistical properties of this framework. Our proposed methodology relies on scalable non-parametric density estimation using fast Fourier transformation. The resulting estimation method is manyfold faster than the classical bandwidth-based density estimation while maintaining comparable mean integrated squared error rates. We investigate key asymptotic properties of our methodology and introduce a data-splitting technique to facilitate inference. The key attraction of our framework is its inference toolkit, which allows researchers to quantify uncertainty in causal discovery findings. We illustrate the performance of our methodology through simulation studies as well as multiple real data examples.},\n\turldate = {2023-11-09},\n\tpublisher = {arXiv},\n\tauthor = {Purkayastha, Soumik and Song, Peter X.-K.},\n\tmonth = nov,\n\tyear = {2023},\n\tnote = {arXiv:2311.04696 null},\n\tkeywords = {Statistics - Methodology, Mathematics - Statistics Theory},\n\tfile = {arXiv Fulltext PDF:/Users/soumikp/Zotero/storage/LUM5UQ4G/Purkayastha and Song - 2023 - Generative causality using Shannon's information .pdf:application/pdf;arXiv.org Snapshot:/Users/soumikp/Zotero/storage/EUNA56Y3/2311.html:text/html},\n}\n\n
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\n Causal investigations in observational studies pose a great challenge in scientific research where randomized trials or intervention-based studies are not feasible. Leveraging Shannon's seminal work on information theory, we consider a framework of asymmetry where any causal link between putative cause and effect must be explained through a mechanism governing the cause as well as a generative process yielding an effect of the cause. Under weak assumptions, this framework enables the assessment of whether X is a stronger predictor of Y or vice-versa. Under stronger identifiability assumptions our framework is able to distinguish between cause and effect using observational data. We establish key statistical properties of this framework. Our proposed methodology relies on scalable non-parametric density estimation using fast Fourier transformation. The resulting estimation method is manyfold faster than the classical bandwidth-based density estimation while maintaining comparable mean integrated squared error rates. We investigate key asymptotic properties of our methodology and introduce a data-splitting technique to facilitate inference. The key attraction of our framework is its inference toolkit, which allows researchers to quantify uncertainty in causal discovery findings. We illustrate the performance of our methodology through simulation studies as well as multiple real data examples.\n
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\n \n\n \n \n \n \n \n \n On minimum Bregman divergence inference.\n \n \n \n \n\n\n \n Purkayastha, S.; and Basu, A.\n\n\n \n\n\n\n . 2023.\n Publisher: arXiv\n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{purkayastha_minimum_2023,\n\ttitle = {On minimum {Bregman} divergence inference},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://arxiv.org/abs/2008.06987},\n\tdoi = {10.48550/ARXIV.2008.06987},\n\tabstract = {In this paper a new family of minimum divergence estimators based on the Bregman divergence is proposed. The popular density power divergence (DPD) class of estimators is a sub-class of Bregman divergences. We propose and study a new sub-class of Bregman divergences called the exponentially weighted divergence (EWD). Like the minimum DPD estimator, the minimum EWD estimator is recognised as an M-estimator. This characterisation is useful while discussing the asymptotic behaviour as well as the robustness properties of this class of estimators. Performances of the two classes are compared -- both through simulations as well as through real life examples. We develop an estimation process not only for independent and homogeneous data, but also for non-homogeneous data. General tests of parametric hypotheses based on the Bregman divergences are also considered. We establish the asymptotic null distribution of our proposed test statistic and explore its behaviour when applied to real data. The inference procedures generated by the new EWD divergence appear to be competitive or better that than the DPD based procedures.},\n\turldate = {2024-03-21},\n\tauthor = {Purkayastha, Soumik and Basu, Ayanendranath},\n\tyear = {2023},\n\tnote = {Publisher: arXiv},\n\tkeywords = {FOS: Computer and information sciences, FOS: Mathematics, Methodology (stat.ME), Primary 62F10, 62F35, 62F03, Secondary 62J05, Statistics Theory (math.ST)},\n}\n
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\n In this paper a new family of minimum divergence estimators based on the Bregman divergence is proposed. The popular density power divergence (DPD) class of estimators is a sub-class of Bregman divergences. We propose and study a new sub-class of Bregman divergences called the exponentially weighted divergence (EWD). Like the minimum DPD estimator, the minimum EWD estimator is recognised as an M-estimator. This characterisation is useful while discussing the asymptotic behaviour as well as the robustness properties of this class of estimators. Performances of the two classes are compared – both through simulations as well as through real life examples. We develop an estimation process not only for independent and homogeneous data, but also for non-homogeneous data. General tests of parametric hypotheses based on the Bregman divergences are also considered. We establish the asymptotic null distribution of our proposed test statistic and explore its behaviour when applied to real data. The inference procedures generated by the new EWD divergence appear to be competitive or better that than the DPD based procedures.\n
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\n \n\n \n \n \n \n \n \n Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience.\n \n \n \n \n\n\n \n Salvatore, M.; Purkayastha, S.; Ganapathi, L.; Bhattacharyya, R.; Kundu, R.; Zimmermann, L.; Ray, D.; Hazra, A.; Kleinsasser, M.; Solomon, S.; Subbaraman, R.; and Mukherjee, B.\n\n\n \n\n\n\n Science Advances, 8(24): eabp8621. June 2022.\n Publisher: American Association for the Advancement of Science\n\n\n\n
\n\n\n\n \n \n \"LessonsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{salvatore_lessons_2022,\n\ttitle = {Lessons from {SARS}-{CoV}-2 in {India}: {A} data-driven framework for pandemic resilience},\n\tvolume = {8},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tshorttitle = {Lessons from {SARS}-{CoV}-2 in {India}},\n\turl = {https://www.science.org/doi/full/10.1126/sciadv.abp8621},\n\tdoi = {10.1126/sciadv.abp8621},\n\tabstract = {India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.},\n\tnumber = {24},\n\turldate = {2023-04-27},\n\tjournal = {Science Advances},\n\tauthor = {Salvatore, Maxwell and Purkayastha, Soumik and Ganapathi, Lakshmi and Bhattacharyya, Rupam and Kundu, Ritoban and Zimmermann, Lauren and Ray, Debashree and Hazra, Aditi and Kleinsasser, Michael and Solomon, Sunil and Subbaraman, Ramnath and Mukherjee, Bhramar},\n\tmonth = jun,\n\tyear = {2022},\n\tnote = {Publisher: American Association for the Advancement of Science},\n\tpages = {eabp8621},\n\tannote = {Publisher: American Association for the Advancement of Science},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/VL9KA37W/Salvatore et al. - 2022 - Lessons from SARS-CoV-2 in India A data-driven fr.pdf:application/pdf},\n}\n\n
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\n India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.\n
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\n \n\n \n \n \n \n \n \n Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.\n \n \n \n \n\n\n \n Bhaduri, R.; Kundu, R.; Purkayastha, S.; Kleinsasser, M.; Beesley, L. J.; Mukherjee, B.; and Datta, J.\n\n\n \n\n\n\n Statistics in Medicine, 41(13): 2317–2337. 2022.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9357\n\n\n\n
\n\n\n\n \n \n \"ExtendingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{bhaduri_extending_2022,\n\ttitle = {Extending the susceptible-exposed-infected-removed ({SEIR}) model to handle the false negative rate and symptom-based administration of {COVID}-19 diagnostic tests: {SEIR}-fansy},\n\tvolume = {41},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {1097-0258},\n\tshorttitle = {Extending the susceptible-exposed-infected-removed ({SEIR}) model to handle the false negative rate and symptom-based administration of {COVID}-19 diagnostic tests},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9357},\n\tdoi = {10.1002/sim.9357},\n\tabstract = {False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported “case” counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95\\% CI: 10.7,11.5) and for deaths at 3.58 (95\\% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95\\% CI: 17.9, 19.9) and 4.55 (95\\% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0\\% (95\\% CI: 8.7\\%, 9.3\\%) and 5.2\\% (95\\% CI: 5.0\\%, 5.6\\%) of total estimated infections were reported on these two dates, while 27.9\\% (95\\% CI: 27.3\\%, 28.6\\%) and 22\\% (95\\% CI: 21.4\\%, 23.1\\%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.},\n\tlanguage = {en},\n\tnumber = {13},\n\turldate = {2023-04-27},\n\tjournal = {Statistics in Medicine},\n\tauthor = {Bhaduri, Ritwik and Kundu, Ritoban and Purkayastha, Soumik and Kleinsasser, Michael and Beesley, Lauren J. and Mukherjee, Bhramar and Datta, Jyotishka},\n\tyear = {2022},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9357},\n\tkeywords = {compartmental models, infection fatality rate, R package SEIRfansy, reproduction number, selection bias, sensitivity, undetected infections},\n\tpages = {2317--2337},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/4HR6KYYG/Bhaduri et al. - 2022 - Extending the susceptible-exposed-infected-removed.pdf:application/pdf},\n}\n\n
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\n False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported “case” counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.\n
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\n \n\n \n \n \n \n \n \n A comparison of five epidemiological models for transmission of SARS-CoV-2 in India.\n \n \n \n \n\n\n \n Purkayastha, S.; Bhattacharyya, R.; Bhaduri, R.; Kundu, R.; Gu, X.; Salvatore, M.; Ray, D.; Mishra, S.; and Mukherjee, B.\n\n\n \n\n\n\n BMC Infectious Diseases, 21(1): 533. June 2021.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{purkayastha_comparison_2021,\n\ttitle = {A comparison of five epidemiological models for transmission of {SARS}-{CoV}-2 in {India}},\n\tvolume = {21},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {1471-2334},\n\turl = {https://doi.org/10.1186/s12879-021-06077-9},\n\tdoi = {10.1186/s12879-021-06077-9},\n\tabstract = {Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).},\n\tnumber = {1},\n\turldate = {2023-04-27},\n\tjournal = {BMC Infectious Diseases},\n\tauthor = {Purkayastha, Soumik and Bhattacharyya, Rupam and Bhaduri, Ritwik and Kundu, Ritoban and Gu, Xuelin and Salvatore, Maxwell and Ray, Debashree and Mishra, Swapnil and Mukherjee, Bhramar},\n\tmonth = jun,\n\tyear = {2021},\n\tkeywords = {Compartmental models, Low and middle income countries, Prediction uncertainty, Statistical models},\n\tpages = {533},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/Q56ZUKFN/Purkayastha et al. - 2021 - A comparison of five epidemiological models for tr.pdf:application/pdf},\n}\n\n
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\n Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM).\n
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\n \n\n \n \n \n \n \n \n Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India.\n \n \n \n \n\n\n \n Purkayastha, S.; Kundu, R.; Bhaduri, R.; Barker, D.; Kleinsasser, M.; Ray, D.; and Mukherjee, B.\n\n\n \n\n\n\n BMC Research Notes, 14(1): 262. July 2021.\n \n\n\n\n
\n\n\n\n \n \n \"EstimatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{purkayastha_estimating_2021,\n\ttitle = {Estimating the wave 1 and wave 2 infection fatality rates from {SARS}-{CoV}-2 in {India}},\n\tvolume = {14},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {1756-0500},\n\turl = {https://doi.org/10.1186/s13104-021-05652-2},\n\tdoi = {10.1186/s13104-021-05652-2},\n\tabstract = {There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020–January 31, 2021) and part of the second wave (February 1, 2021–May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations.},\n\tnumber = {1},\n\turldate = {2023-04-27},\n\tjournal = {BMC Research Notes},\n\tauthor = {Purkayastha, Soumik and Kundu, Ritoban and Bhaduri, Ritwik and Barker, Daniel and Kleinsasser, Michael and Ray, Debashree and Mukherjee, Bhramar},\n\tmonth = jul,\n\tyear = {2021},\n\tkeywords = {India, Case fatality rate, Excess deaths, False negative rates, RT-PCR test, SEIR model, Underreporting},\n\tpages = {262},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/X4ARMBBB/Purkayastha et al. - 2021 - Estimating the wave 1 and wave 2 infection fatalit.pdf:application/pdf},\n}\n\n
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\n There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020–January 31, 2021) and part of the second wave (February 1, 2021–May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations.\n
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\n \n\n \n \n \n \n \n \n SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation.\n \n \n \n \n\n\n \n Zimmermann, L.; Bhattacharya, S.; Purkayastha, S.; Kundu, R.; Bhaduri, R.; Ghosh, P.; and Mukherjee, B.\n\n\n \n\n\n\n Studies in Microeconomics, 9(2): 137–179. December 2021.\n Publisher: SAGE Publications India\n\n\n\n
\n\n\n\n \n \n \"SARS-CoV-2Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{zimmermann_sars-cov-2_2021,\n\ttitle = {{SARS}-{CoV}-2 {Infection} {Fatality} {Rates} in {India}: {Systematic} {Review}, {Meta}-analysis and {Model}-based {Estimation}},\n\tvolume = {9},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {2321-0222},\n\tshorttitle = {{SARS}-{CoV}-2 {Infection} {Fatality} {Rates} in {India}},\n\turl = {https://doi.org/10.1177/23210222211054324},\n\tdoi = {10.1177/23210222211054324},\n\tabstract = {Introduction:Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation?s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021.Methods:Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021.Results:For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097\\% (95\\% confidence interval [CI]: 0.067?0.140) and 0.365\\% (95\\% CI: 0.264?0.504) to 0.485\\% (95\\% CI: 0.344?0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055\\%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290?1.316) to (0.241?0.651)\\%).Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101\\% (95\\% CI: 0.097?0.116) and 0.367\\% (95\\% CI: 0.358?0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1.Conclusion:When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48\\%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7?4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35\\%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.JEL Classifications: I15, I18},\n\tnumber = {2},\n\turldate = {2023-04-27},\n\tjournal = {Studies in Microeconomics},\n\tauthor = {Zimmermann, Lauren and Bhattacharya, Subarna and Purkayastha, Soumik and Kundu, Ritoban and Bhaduri, Ritwik and Ghosh, Parikshit and Mukherjee, Bhramar},\n\tmonth = dec,\n\tyear = {2021},\n\tnote = {Publisher: SAGE Publications India},\n\tpages = {137--179},\n\tannote = {Publisher: SAGE Publications India},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/BA94GLMM/Zimmermann et al. - 2021 - SARS-CoV-2 Infection Fatality Rates in India Syst.pdf:application/pdf},\n}\n\n
\n
\n\n\n
\n Introduction:Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation?s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021.Methods:Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021.Results:For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067?0.140) and 0.365% (95% CI: 0.264?0.504) to 0.485% (95% CI: 0.344?0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290?1.316) to (0.241?0.651)%).Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097?0.116) and 0.367% (95% CI: 0.358?0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1.Conclusion:When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7?4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.JEL Classifications: I15, I18\n
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\n \n\n \n \n \n \n \n \n Discussion on “The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method” by Tian et al..\n \n \n \n \n\n\n \n Purkayastha, S.; and Song, P.\n\n\n \n\n\n\n Statistics and Its Interface, 14(1): 21–22. 2021.\n Publisher: International Press of Boston\n\n\n\n
\n\n\n\n \n \n \"DiscussionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{purkayastha_discussion_2021,\n\ttitle = {Discussion on “{The} timing and effectiveness of implementing mild interventions of {COVID}-19 in large industrial regions via a synthetic control method” by {Tian} \\textit{et al.}},\n\tvolume = {14},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {1938-7997},\n\turl = {https://www.intlpress.com/site/pub/pages/journals/items/sii/content/vols/0014/0001/a005/abstract.php},\n\tdoi = {10.4310/20-SII652},\n\tabstract = {International Press of Boston - publishers of scholarly mathematical and scientific journals and books},\n\tlanguage = {EN},\n\tnumber = {1},\n\turldate = {2023-04-27},\n\tjournal = {Statistics and Its Interface},\n\tauthor = {Purkayastha, Soumik and Song, Peter},\n\tyear = {2021},\n\tnote = {Publisher: International Press of Boston},\n\tpages = {21--22},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/PF928DYF/Purkayastha and Song - 2021 - Discussion on “The timing and effectiveness of imp.pdf:application/pdf},\n}\n\n
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\n International Press of Boston - publishers of scholarly mathematical and scientific journals and books\n
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\n  \n 2020\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms.\n \n \n \n\n\n \n Ray, D.; Salvatore, M.; Bhattacharyya, R.; Wang, L.; Du, J.; Mohammed, S.; Purkayastha, S.; Halder, A.; Rix, A.; Barker, D.; Kleinsasser, M.; Zhou, Y.; Bose, D.; Song, P.; Banerjee, M.; Baladandayuthapani, V.; Ghosh, P.; and Mukherjee, B.\n\n\n \n\n\n\n Harvard Data Science Review, 2020(Suppl 1). 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{ray_predictions_2020,\n\ttitle = {Predictions, role of interventions and effects of a historic national lockdown in {India}'s response to the {COVID}-19 pandemic: data science call to arms},\n\tvolume = {2020},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {2644-2353},\n\tshorttitle = {Predictions, role of interventions and effects of a historic national lockdown in {India}'s response to the {COVID}-19 pandemic},\n\tdoi = {10.1162/99608f92.60e08ed5},\n\tabstract = {With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.},\n\tlanguage = {eng},\n\tnumber = {Suppl 1},\n\tjournal = {Harvard Data Science Review},\n\tauthor = {Ray, Debashree and Salvatore, Maxwell and Bhattacharyya, Rupam and Wang, Lili and Du, Jiacong and Mohammed, Shariq and Purkayastha, Soumik and Halder, Aritra and Rix, Alexander and Barker, Daniel and Kleinsasser, Michael and Zhou, Yiwang and Bose, Debraj and Song, Peter and Banerjee, Mousumi and Baladandayuthapani, Veerabhadran and Ghosh, Parikshit and Mukherjee, Bhramar},\n\tyear = {2020},\n\tpmid = {32607504},\n\tpmcid = {PMC7326342},\n\tkeywords = {Basic reproduction number, Coronavirus, Credible interval, India, Intervention forecasting, SIR model, mine},\n\tfile = {Full Text:/Users/soumikp/Zotero/storage/KTIBESD4/Ray et al. - 2020 - Predictions, role of interventions and effects of .pdf:application/pdf},\n}\n\n
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\n With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially \"flatten the curve\" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.\n
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\n \n\n \n \n \n \n \n \n A Review of Multi-Compartment Infectious Disease Models.\n \n \n \n \n\n\n \n Tang, L.; Zhou, Y.; Wang, L.; Purkayastha, S.; Zhang, L.; He, J.; Wang, F.; and Song, P. X.\n\n\n \n\n\n\n International Statistical Review, 88(2): 462–513. 2020.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12402\n\n\n\n
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@article{tang_review_2020,\n\ttitle = {A {Review} of {Multi}-{Compartment} {Infectious} {Disease} {Models}},\n\tvolume = {88},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {1751-5823},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12402},\n\tdoi = {10.1111/insr.12402},\n\tabstract = {Multi-compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community-level micromodel that enables high-resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-04-27},\n\tjournal = {International Statistical Review},\n\tauthor = {Tang, Lu and Zhou, Yiwang and Wang, Lili and Purkayastha, Soumik and Zhang, Leyao and He, Jie and Wang, Fei and Song, Peter X.-K.},\n\tyear = {2020},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12402},\n\tkeywords = {antibody, cellular automaton, COVID-19, Markov chain Monte Carlo, risk prediction, spatio-temporal model, state-space model},\n\tpages = {462--513},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/FKH5H2IX/Tang et al. - 2020 - A Review of Multi-Compartment Infectious Disease M.pdf:application/pdf},\n}\n\n
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\n Multi-compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community-level micromodel that enables high-resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.\n
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\n \n\n \n \n \n \n \n \n Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.\n \n \n \n \n\n\n \n Salvatore, M.; Basu, D.; Ray, D.; Kleinsasser, M.; Purkayastha, S.; Bhattacharyya, R.; and Mukherjee, B.\n\n\n \n\n\n\n BMJ Open, 10(12): e041778. December 2020.\n Publisher: British Medical Journal Publishing Group Section: Public health\n\n\n\n
\n\n\n\n \n \n \"ComprehensivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{salvatore_comprehensive_2020,\n\ttitle = {Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on {COVID}-19 spread in {India}: national trends masking state-level variations},\n\tvolume = {10},\n\tcopyright = {© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.},\n\tissn = {2044-6055, 2044-6055},\n\tshorttitle = {Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on {COVID}-19 spread in {India}},\n\turl = {https://bmjopen.bmj.com/content/10/12/e041778},\n\tdoi = {10.1136/bmjopen-2020-041778},\n\tabstract = {Objectives To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.\nDesign Cohort study (daily time series of case counts).\nSetting Observational and population based.\nParticipants Confirmed COVID-19 cases nationally and across 20 states that accounted for {\\textgreater}99\\% of the current cumulative case counts in India until 31 May 2020.\nExposure Lockdown (non-medical intervention).\nMain outcomes and measures We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.\nResults The estimated effective reproduction number R for India was 3.36 (95\\% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95\\% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19–25 March) to 113 372 (25–31 May) while the test positivity rate increased from 2.1\\% to 4.2\\%, respectively. However, various states exhibit substantial departures from these national patterns.\nConclusions Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2023-04-27},\n\tjournal = {BMJ Open},\n\tauthor = {Salvatore, Maxwell and Basu, Deepankar and Ray, Debashree and Kleinsasser, Mike and Purkayastha, Soumik and Bhattacharyya, Rupam and Mukherjee, Bhramar},\n\tmonth = dec,\n\tyear = {2020},\n\tpmid = {33303462},\n\tnote = {Publisher: British Medical Journal Publishing Group\nSection: Public health},\n\tkeywords = {epidemiology, public health, statistics \\& research methods},\n\tpages = {e041778},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/X475SGEI/Salvatore et al. - 2020 - Comprehensive public health evaluation of lockdown.pdf:application/pdf},\n}\n\n
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\n Objectives To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics. Design Cohort study (daily time series of case counts). Setting Observational and population based. Participants Confirmed COVID-19 cases nationally and across 20 states that accounted for \\textgreater99% of the current cumulative case counts in India until 31 May 2020. Exposure Lockdown (non-medical intervention). Main outcomes and measures We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing. Results The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19–25 March) to 113 372 (25–31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns. Conclusions Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.\n
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\n \n\n \n \n \n \n \n \n A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States.\n \n \n \n \n\n\n \n Zhou, Y.; Wang, L.; Zhang, L.; Shi, L.; Yang, K.; He, J.; Bangyao, Z.; Overton, W.; Purkayastha, S.; and Song, P.\n\n\n \n\n\n\n Harvard Data Science Review, (Special Issue 1). May 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhou_spatiotemporal_2020,\n\ttitle = {A {Spatiotemporal} {Epidemiological} {Prediction} {Model} to {Inform} {County}-{Level} {COVID}-19 {Risk} in the {United} {States}},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {2644-2353, 2688-8513},\n\turl = {https://hdsr.mitpress.mit.edu/pub/qqg19a0r/release/1},\n\tdoi = {10.1162/99608f92.79e1f45e},\n\tabstract = {As the COVID-19 pandemic continues worsening in the US, it is of critical importance to develop a health information system that provides timely risk evaluation and prediction of the COVID-19 infection in communities. We propose a spatiotemporal epidemiological forecast model that combines a spatial cellular automata (CA) with a temporal extended Susceptible-Antibody-Infectious-Removed (eSAIR) model under time-varying state-specific control measures. This new toolbox enables projection of the county-level COVID-19 prevalence over 3109 counties in the continental US, including t-day ahead risk forecast and the risk related to a travel route. In comparison to existing temporal risk prediction models, the proposed CA-eSAIR model informs projected county-level risk to governments and residents of the local coronavirus spread patterns and the associated personal risks at specifi c geolocations. Such high-resolution risk projection is useful for decision-making on business reopening and resource allocation for COVID-19 tests.},\n\tlanguage = {en},\n\tnumber = {Special Issue 1},\n\turldate = {2023-04-27},\n\tjournal = {Harvard Data Science Review},\n\tauthor = {Zhou, Yiwang and Wang, Lili and Zhang, Leyao and Shi, Lan and Yang, Kangping and He, Jie and Bangyao, Zhao and Overton, William and Purkayastha, Soumik and Song, Peter},\n\tmonth = may,\n\tyear = {2020},\n\tfile = {Full Text PDF:/Users/soumikp/Zotero/storage/E2JCJALZ/Zhou et al. - 2020 - A Spatiotemporal Epidemiological Prediction Model .pdf:application/pdf},\n}\n\n
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\n As the COVID-19 pandemic continues worsening in the US, it is of critical importance to develop a health information system that provides timely risk evaluation and prediction of the COVID-19 infection in communities. We propose a spatiotemporal epidemiological forecast model that combines a spatial cellular automata (CA) with a temporal extended Susceptible-Antibody-Infectious-Removed (eSAIR) model under time-varying state-specific control measures. This new toolbox enables projection of the county-level COVID-19 prevalence over 3109 counties in the continental US, including t-day ahead risk forecast and the risk related to a travel route. In comparison to existing temporal risk prediction models, the proposed CA-eSAIR model informs projected county-level risk to governments and residents of the local coronavirus spread patterns and the associated personal risks at specifi c geolocations. Such high-resolution risk projection is useful for decision-making on business reopening and resource allocation for COVID-19 tests.\n
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\n \n\n \n \n \n \n \n \n Are women leaders significantly better at controlling the contagion during the COVID-19 pandemic?.\n \n \n \n \n\n\n \n Purkayastha, S.; Salvatore, M.; and Mukherjee, B.\n\n\n \n\n\n\n Journal of health and social sciences, 5(2): 231–240. June 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ArePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{purkayastha_are_2020,\n\ttitle = {Are women leaders significantly better at controlling the contagion during the {COVID}-19 pandemic?},\n\tvolume = {5},\n\tcopyright = {Creative Commons Attribution-NonCommercial 4.0 International Licence (CC-BY-NC)},\n\tissn = {2499-2240},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457824/},\n\tabstract = {Recent media articles have suggested that women-led countries are doing better in terms of their responses to the COVID-19 pandemic. We examine an ensemble of public health metrics to assess the control of COVID-19 epidemic in women-versus men-led countries worldwide based on data available up to June 3. The median of the distribution of median time-varying effective reproduction number for women- and men-led countries were 0.89 and 1.14 respectively with the 95\\% two-sample bootstrap-based confidence interval for the difference (women – men) being [−0.34, 0.02]. In terms of scale of testing, the median percentage of population tested were 3.28\\% (women), 1.59\\% (men) [95\\% CI: (−1.29\\%, 3.60\\%)] with test positive rates of 2.69\\% (women) and 4.94\\% (men) respectively. It appears that though statistically not significant, countries led by women have an edge over countries led by men in terms of public health metrics for controlling the spread of the COVID-19 pandemic worldwide.},\n\tnumber = {2},\n\turldate = {2023-04-27},\n\tjournal = {Journal of health and social sciences},\n\tauthor = {Purkayastha, Soumik and Salvatore, Maxwell and Mukherjee, Bhramar},\n\tmonth = jun,\n\tyear = {2020},\n\tpmid = {32875269},\n\tpmcid = {PMC7457824},\n\tpages = {231--240},\n\tfile = {PubMed Central Full Text PDF:/Users/soumikp/Zotero/storage/29GC5FI4/Purkayastha et al. - 2020 - Are women leaders significantly better at controll.pdf:application/pdf},\n}\n\n
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\n Recent media articles have suggested that women-led countries are doing better in terms of their responses to the COVID-19 pandemic. We examine an ensemble of public health metrics to assess the control of COVID-19 epidemic in women-versus men-led countries worldwide based on data available up to June 3. The median of the distribution of median time-varying effective reproduction number for women- and men-led countries were 0.89 and 1.14 respectively with the 95% two-sample bootstrap-based confidence interval for the difference (women – men) being [−0.34, 0.02]. In terms of scale of testing, the median percentage of population tested were 3.28% (women), 1.59% (men) [95% CI: (−1.29%, 3.60%)] with test positive rates of 2.69% (women) and 4.94% (men) respectively. It appears that though statistically not significant, countries led by women have an edge over countries led by men in terms of public health metrics for controlling the spread of the COVID-19 pandemic worldwide.\n
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\n \n\n \n \n \n \n \n Prediction of monthly Hilsa (Tenualosa ilisha) catch in the Northern Bay of Bengal using Bayesian structural time series model.\n \n \n \n\n\n \n Giri, S.; Purkayastha, S.; Hazra, S.; Chanda, A.; Das, I.; and Das, S.\n\n\n \n\n\n\n Regional Studies in Marine Science, 39: 101456. 2020.\n Publisher: Elsevier\n\n\n\n
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@article{giri_prediction_2020,\n\ttitle = {Prediction of monthly {Hilsa} ({Tenualosa} ilisha) catch in the {Northern} {Bay} of {Bengal} using {Bayesian} structural time series model},\n\tvolume = {39},\n\tjournal = {Regional Studies in Marine Science},\n\tauthor = {Giri, Sandip and Purkayastha, Soumik and Hazra, Sugata and Chanda, Abhra and Das, Isha and Das, Sourav},\n\tyear = {2020},\n\tnote = {Publisher: Elsevier},\n\tkeywords = {Bay of Bengal, Chlorophyll, CPUE, Hilsa, Monthly prediction, Rainfall},\n\tpages = {101456},\n\tannote = {Publisher: Elsevier},\n\tfile = {ScienceDirect Full Text PDF:/Users/soumikp/Zotero/storage/JFTC2GN4/Giri et al. - 2020 - Prediction of monthly Hilsa (Tenualosa ilisha) cat.pdf:application/pdf},\n}\n\n
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