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Coronavirus threat to Indian population: risk factors, transmission dynamics and preparedness to prevent the spread of the virus.

ABSTRACT: Recent novel coronavirus outbreak in Wuhan City of Hubei province in China infected nearly 70,000 individuals and killed more than 1700 people within a short span of time leading to global pandemic. The disease is now spread to 26 countries in Asia, North America, Europe and Australasia. The virus is spreading rapidly to Asia-pacific and Southeast Asian countries. The disease is posing a serious threat to human population and has devastating impact on public health and economy. So far 3 Indians are infected and India is at risk of rapid spread of the disease because of its geographical location and other favorable conditions. With a poorer global health security index compared to China (India-57 and China-51), any such situation will have worse outcome. In near future there are also possibilities of similar kind of disease outbreak caused by new strains of coronaviruses due to factors like species jump of new viruses, high population density and inadequate medical facilities. In this short review we have highlighted the risk factors and transmission dynamics of coronaviruses that may pose a serious threat to India. We have also discussed about the possible preventive measure our country should take to control any such outbreak situation.

SUBMITTER: Mandal D

PROVIDER: S-EPMC7167493 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Coronavirus threat to Indian population: risk factors, transmission dynamics and preparedness to prevent the spread of the virus.

Mandal Dibyakanti D

Virusdisease 20200420 2

Recent novel coronavirus outbreak in Wuhan City of Hubei province in China infected nearly 70,000 individuals and killed more than 1700 people within a short span of time leading to global pandemic. The disease is now spread to 26 countries in Asia, North America, Europe and Australasia. The virus is spreading rapidly to Asia-pacific and Southeast Asian countries. The disease is posing a serious threat to human population and has devastating impact on public health and economy. So far 3 Indians ...[more]

PMID: 32313825

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Project description:BackgroundMobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture.MethodsIn Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion.ResultsIn the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time.ConclusionOur results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.

Dataset Information

Coronavirus threat to Indian population: risk factors, transmission dynamics and preparedness to prevent the spread of the virus.

Publications

Coronavirus threat to Indian population: risk factors, transmission dynamics and preparedness to prevent the spread of the virus.

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