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      Epidemiology and transmission dynamics of COVID-19 in two Indian states

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          Epidemiology in southern India

          By August 2020, India had reported several million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with cases tending to show a younger age distribution than has been reported in higher-income countries. Laxminarayan et al. analyzed data from the Indian states of Tamil Nadu and Andhra Pradesh, which have developed rigorous contact tracing and testing systems (see the Perspective by John and Kang). Superspreading predominated, with 5% of infected individuals accounting for 80% of cases. Enhanced transmission risk was apparent among children and young adults, who accounted for one-third of cases. Deaths were concentrated in 50- to 64-year-olds. Incidence did not change in older age groups, possibly because of effective stay-at-home orders and social welfare programs or socioeconomic status. As in other settings, however, mortality rates were associated with older age, comorbidities, and being male.

          Science, this issue p. 691; see also p. [Related article:]663

          Abstract

          The epidemiology of SARS-CoV-2 in southern India shows marked differences from that observed in higher-income countries.

          Abstract

          Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

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          Most cited references10

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          A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

          Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
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            Superspreading and the effect of individual variation on disease emergence

            Coughs and sneezes... From Typhoid Mary to SARS, it has long been known that some people spread disease more than others. But for diseases transmitted via casual contact, contagiousness arises from a plethora of social and physiological factors, so epidemiologists have tended to rely on population averages to assess a disease's potential to spread. A new analysis of outbreak data shows that individual differences in infectiousness exert powerful influences on the epidemiology of ten deadly diseases. SARS and measles (and perhaps avian influenza) show strong tendencies towards ‘superspreading events’ that can ignite explosive epidemics — but this same volatility makes outbreaks more likely to fizzle out. Smallpox and pneumonic plague, two potential bioterrorism agents, show steadier growth but still differ markedly from the traditional average-based view. These findings are relevant to how emerging diseases are detected and controlled. Supplementary information The online version of this article (doi:10.1038/nature04153) contains supplementary material, which is available to authorized users.
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              Incubation periods of acute respiratory viral infections: a systematic review

              Summary Knowledge of the incubation period is essential in the investigation and control of infectious disease, but statements of incubation period are often poorly referenced, inconsistent, or based on limited data. In a systematic review of the literature on nine respiratory viral infections of public-health importance, we identified 436 articles with statements of incubation period and 38 with data for pooled analysis. We fitted a log-normal distribution to pooled data and found the median incubation period to be 5·6 days (95% CI 4·8–6·3) for adenovirus, 3·2 days (95% CI 2·8–3·7) for human coronavirus, 4·0 days (95% CI 3·6–4·4) for severe acute respiratory syndrome coronavirus, 1·4 days (95% CI 1·3–1·5) for influenza A, 0·6 days (95% CI 0·5–0·6) for influenza B, 12·5 days (95% CI 11·8–13·3) for measles, 2·6 days (95% CI 2·1–3·1) for parainfluenza, 4·4 days (95% CI 3·9–4·9) for respiratory syncytial virus, and 1·9 days (95% CI 1·4–2·4) for rhinovirus. When using the incubation period, it is important to consider its full distribution: the right tail for quarantine policy, the central regions for likely times and sources of infection, and the full distribution for models used in pandemic planning. Our estimates combine published data to give the detail necessary for these and other applications.
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                Author and article information

                Journal
                Science
                Science
                SCIENCE
                science
                Science (New York, N.y.)
                American Association for the Advancement of Science
                0036-8075
                1095-9203
                06 November 2020
                30 September 2020
                : 370
                : 6517
                : 691-697
                Affiliations
                [1 ]Center for Disease Dynamics, Economics and Policy, New Delhi, India.
                [2 ]Princeton Environmental Institute, Princeton University, Princeton, NJ, USA.
                [3 ]Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
                [4 ]International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
                [5 ]Department of Community Medicine, Government Medical College, Kadapa, Andhra Pradesh, India.
                [6 ]Animal Husbandry, Dairying and Fisheries Department, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
                [7 ]Backward Classes, Most Backward Classes, and Minorities Welfare Department, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
                [8 ]Department of Community Medicine, Guntur Medical College, Guntur, Andhra Pradesh, India.
                [9 ]Department of Health, Family Welfare, and Medical Education, Government of Andhra Pradesh, Amaravati, Andhra Pradesh, India.
                [10 ]Health and Family Welfare Department, Government of Tamil Nadu, Chennai, Tamil Nadu, India.
                [11 ]Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
                [12 ]Center for Computational Biology, College of Engineering, University of California, Berkeley, CA, USA.
                Author notes
                [* ]Corresponding author. Email: jlewnard@ 123456berkeley.edu
                Author information
                https://orcid.org/0000-0002-1390-9016
                https://orcid.org/0000-0003-0202-8798
                https://orcid.org/0000-0002-9236-093X
                https://orcid.org/0000-0003-2442-3780
                https://orcid.org/0000-0002-5565-5894
                https://orcid.org/0000-0002-8505-8839
                Article
                abd7672
                10.1126/science.abd7672
                7857399
                33154136
                3e89ddba-e81f-49b8-bd47-ff88ff776e0f
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 July 2020
                : 23 September 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: CCF-1918628
                Funded by: doi http://dx.doi.org/10.13039/100000030, Centers for Disease Control and Prevention;
                Award ID: 16IPA16092427
                Funded by: doi http://dx.doi.org/10.13039/100009633, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
                Award ID: P2CHD073964
                Categories
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                Caroline Ash
                Jeff Cook

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