A steady trickle-down from metro districts and improving epidemic-parameters characterized the increasing COVID-19 cases in India

Abstract

Background. By mid-September of 2020, the number of daily new infections in India have crossed 95,000. To facilitate an intuition for the spatio-temporal development of the pandemic and to help resource deployment planning, we analyze and describe how the disease burden almost-predictably shifted from large metropolitan districts to sub-urban districts. Methods. We gathered the publicly available granular data from 186 different districts (equivalent of counties) on their COVID-19 infections and deaths during the 15 April to 31 August 2020 period. These districts presented an active case burden of 559,566 and a cumulative 2,715,656 infections as of August 31. The epidemiological data of these districts was fit to a susceptible-asymptomatic-infected-recovered-dead (SAIRD) model and the underlying epidemic parameters for each of these districts during the course of 4 months was estimated. We validated these parameters against known epidemiological characteristic distributions and analyzed them to understand their changes in space-time during the pandemic. Findings. The center of the burden of the current-active infections which on May 15 was in the large metro districts with most international access shifted continuously and smoothly shifted towards districts which could be accessed by domestic airports and by trains. A linear trend-analysis showed a continuous improvement in most epidemic parameters consistently across the districts with four categories of accessibility from an international travel perspective - large metro, metro, urban and sub-urban districts. The reproduction numbers improved from 1.77±0.58 on May 15 to 1.07± 0.13 on August 31 in large metro districts (p-Value of trend 0.0001053); and from 1.58±0.39 on May 15 to 0.94±0.11 on August 31 in sub-urban districts (p-Value of trend 0.0067). The recovery rate per infected person per day improved from 0.0581±0.009 on May 15 to 0.091±0.010 on August 31 in large metro districts (p-Value of trend 0.26.10^{-12}); and from 0.059±0.011 on May 15 to 0.100±0.010 on August 31 in sub-urban districts (p-Value of trend 0.12.10{-16}). The death rate of symptomatic individuals which includes the case-fatality-rate as well as the time from symptoms to death, consistently decreased from 0.0025±0.0014 on May 15 to 0.0013±0.0003 on August 31 in large metro districts (p-Value of trend 0.0010); and from 0.0018±0.0008 on May 15 to 0.0014±0.0003 on August 31 in sub-urban districts (p-Value of trend 0.2789. Interpretation. As the daily infections continue to rise at a national level, it is important to notice a `local-flattening' in larger metro districts, and a shift of the pandemic-burden towards smaller sized districts in a clear hierarchical fashion of accessibility from an international travel perspective. The pandemic burden shifting towards remotely accessible regions, with possibly lesser health care facilities, is a call for attention to the re-organization of resources.

Temporal Analysis Of COVID-19 Cases And Recovery Trends In Panvel City: A Spline Curve Approach

“…The study observes a continuous improvement in epidemic parameters, including reproduction numbers, recovery rates, and death rates, with a noteworthy trend of the pandemic burden moving towards regions accessible by domestic airports and trains. The findings emphasize the importance of resource reorganization to address the evolving geographic distribution of the pandemic [3]. Conducting a meta-analysis across 36 European countries, KaradaÄŸ investigates COVID-19 case increase, case fatality, and case recovery rates.…”

Using high effective risk of Adult-Senior duo in multigenerational homes to prioritize COVID-19 vaccination

“…Thus, with the stock of vaccinations India has, it is feasible to vaccinate almost all of these high risk and high 'effective risk' populations within 2 to 3 months. The strategy can be further refined and made granular by recognizing that the pandemic spread is not uniform across the country, and the case load migrates [Ansumali et al 2020]. It is also possible to preferentially vaccinate these populations with high risk and 'high effective risk' in districts or states that are at the highest risk of COVID-19 infections.…”

The INDSCI-SIM model for COVID-19 in India

“…Agent based models have provided useful insights, at the level of full cities, into mitigation methods and the effectiveness of non-pharmaceutical interventions [12]. Related references which model COVID-19 in India are [10,[13][14][15][16][17][18][19][20][21][22][23][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. These models are very largely compartmental models of varying degrees of complexity [37].…”