COVID-19 has taken the world by storm, with the majority of nations still being challenged by the novel coronavirus. The present work attempts to evaluate the spread of COVID-19 in India using the Susceptible-Exposed-Infectious-Removed (SEIR) model to establish the impact of socio-behavioural aspects, especially social distancing. The impact of environmental factors like temperature and relative humidity (RH) using statistical methods, including Response Surface Methodology (RSM) and Pearson’s correlation, is also studied on numbers of COVID-19 cases per day. Here we report the resultant changes of lockdowns-unlocks initiated by the Government of India for COVID-19, as against the scenario of total lockdown. The phased unlocks and crowded gatherings result in an increase in the number of cases and stretch the mitigation timeline of COVID-19 spread, delaying the flattening of the curve. The SEIR model predictions have been fairly validated against the actual cases. The daily spread of COVID-19 cases is also fairly correlated with temperature in Indian cities, as supported by well-established causation of the role of higher temperatures in disrupting the lipid layer of coronavirus, but is greatly undermined by the key factor of social distancing and gets confounded with other multiple unknown co-varying environmental factors. However, the analysis couldn’t clearly establish the role of RH in affecting daily COVID-19 cases. Hence, it becomes essential to include environmental parameters into epidemiological models like SEIR and to systematically plan controlled laboratory experiments and modeling studies to draw conclusive inferences, assisting policymakers and stakeholders in formulating comprehensive action plans to alleviate the COVID-19 spread.

COVID-19席卷全球，大多数国家仍在受到新型冠状病毒的挑战。目前的工作试图使用易感性传染病去除模型（SEIR）来评估COVID-19在印度的传播，以建立社会行为方面的影响，尤其是社会距离。还使用统计方法（包括响应表面方法学（RSM）和皮尔森相关性）对环境因素（例如温度和相对湿度（RH））的影响进行了每天COVID-19病例数的研究。在这里，我们报告了印度政府针对COVID-19发起的锁定解锁的最终变化，而不是总锁定情况。分阶段的解锁和拥挤的聚会导致案件数量增加，并延长了COVID-19传播的缓解时间线，延迟曲线的平坦化。SEIR模型的预测已针对实际案例进行了充分验证。COVID-19病例的每日传播也与印度城市的温度有相当的相关性，这是由高温导致破坏冠状病毒的脂质层的公认原因所证实的，但是由于社会差异的关键因素极大地破坏了温度并与其他多个未知的共同变化的环境因素混淆。但是，该分析无法清楚地确定RH在影响日常COVID-19病例中的作用。因此，将环境参数纳入流行病学模型（如​​SEIR）并系统地计划受控的实验室实验和建模研究以得出结论性推论至关重要，