The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, INDIA.
Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai, INDIA.
PLoS Comput Biol. 2022 Oct 24;18(10):e1010632. doi: 10.1371/journal.pcbi.1010632. eCollection 2022 Oct.
Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05-0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.
在印度，估算 COVID-19 的负担是困难的，因为难以评估病例和死亡人数的低估程度。在这里，我们使用一个 9 分量、年龄分层、接触结构的流行病学分区模型，我们称之为 INDSCI-SIM 模型，来分析印度第一波 COVID-19 的传播。我们使用 INDSCI-SIM 和贝叶斯方法，根据第一波印度大流行期间的每日报告病例和死亡数据，对模型进行优化拟合，时间跨度为 2020 年 1 月 30 日至 2021 年 2 月 15 日。我们考虑了封锁和其他非药物干预（NPI）、随时间推移检测量的总体增加、病例和死亡人数的低估以及一系列特定年龄的感染死亡率。我们首先使用我们的模型来描述来自卡纳塔克邦所有个别地区的数据，使用血清学调查数据来验证我们的计算结果。然后，我们将这种方法扩展到卡纳塔克邦的汇总数据。我们对德里、孟买、浦那、班加罗尔和钦奈等城市的大流行进展进行建模，然后对印度整体进行建模。我们估计在第一波期间，死亡人数被低估了 2 到 5 倍，收敛到 2.2 作为代表城乡梯度的乘数。我们还估计在第一波结束时，病例数量被低估了 20 到 25 倍。我们对感染死亡率（IFR）的估计值在 0.05-0.15 之间，与之前的估计值大致一致，但远低于为其他中低收入国家估计的值。我们发现，到第一波结束时，印度约有 35%的人口总体上受到感染，这一结果与血清学调查的结果基本一致。这些结果有助于了解印度 COVID-19 的长期轨迹。
