Objectives: Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategy to enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of the lockdown measures taken in the world’s second-most populous country, India, by exploring their relationship with community mobility patterns and the doubling time of COVID-19. Methods: We conducted a retrospective analysis based on community mobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in India between February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, community mobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict the doubling time of COVID-19. Results: Community mobility drastically fell after the lockdown was instituted. The doubling time of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = –0.45 to –0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = –0.89 to –0.95). Population mobility patterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model’s predictive power increased when the stringency index was also added (R2 = 0.73). Conclusions Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system’s capacity to manage.

Objectives: Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India. Methods: The Google search terms used for the analysis were “coronavirus”, “COVID”, “COVID 19”, “corona”, and “virus”. GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms. Results: “Coronavirus” and “corona” were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms “coronavirus” and “corona” was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term “coronavirus”, with 21 days, i.e., the search volume for “coronavirus” peaked 21 days before the peak number of cases reported by the disease surveillance system. Conclusions Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic affected routine healthcare services across all spectra, and tuberculosis (TB) care under the National Tuberculosis Elimination Program have been affected the most. However, evidence available at the community level is minimal. The clinical features, care cascade pathway, and treatment outcomes of TB patients pre- and during/post-COVID-19 pandemic lockdown in a rural community health block in northern India were assessed and compared.Materials and Methods: This was a retrospective cohort study that included all patients diagnosed with TB and initiated treatment under programmatic settings between January 1 and June 30, 2020, in a rural TB unit in northern India. The periods from January 1 to March 23 and March 24 to June 30 were marked as pre-lockdown and during/post-lockdown, respectively.Results: A total of 103 patients were diagnosed and treated for TB during the study period. A significantly higher proportion of pulmonary TB cases were reported during/post-lockdown (43, 82.7%) compared to that pre-lockdown (32, 62.7%), and a higher diagnostic delay was noted during/post-lockdown (35, 81.4%). Through adjusted analysis, patients diagnosed during/post-lockdown period (adjusted risk ratio [aRR], 0.85; 95% confidence interval [CI], 0.73–0.98) and previously treated (aRR, 0.77; 95% CI, 0.60–0.995) had significantly lower favorable treatment outcomes.Conclusions: The symptom and disease (pulmonary/extrapulmonary) pattern have changed during/post-lockdown. The care cascade delays are still high among TB patients, irrespective of the lockdown status. Lockdown had a significant adverse impact on the outcomes of TB treatment.