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: During the coronavirus disease 2019 (COVID-19) pandemic, countries around the world framed specific laws and imposed varying degrees of lockdowns to ensure the maintenance of physical distancing. Understanding changes in temporal and spatial mobility patterns may provide insights into the dynamics of this infectious disease. Therefore, we assessed the efficacy of lockdown measures in 16 countries worldwide by analyzing the relationship between community mobility patterns and the doubling time of COVID-19. Methods: We performed a retrospective record-based analysis of population-level data on the doubling time for COVID-19 and community mobility. The doubling time for COVID-19 was calculated based on the laboratory-confirmed cases reported daily over the study period (from February 15 to May 2, 2020). Principal component analysis (PCA) of six mobility pattern-related variables was conducted. To explain the magnitude of the effect of mobility on the doubling time, a finite linear distributed lag model was fitted. The k-means clustering approach was employed to identify countries with similar patterns in the significant co-efficient of the mobility index, with the optimal number of clusters derived using Elbow’s method. Results: The countries analyzed had reduced mobility in commercial and social places. Reduced mobility had a significant and favorable association with the doubling time of COVID-19—specifically, the greater the mobility reduction, the longer the time taken for the COVID-19 cases to double. Conclusions COVID-19 lockdowns achieved the immediate objective of mobility reduction in countries with a high burden of cases.

Objective: Early detection and effective management of high-risk pregnancies can substantially contribute to the reduction of adverse maternal and fetal outcomes. This study aimed to determine the prevalence and clinical profile of women with high-risk pregnancies in rural areas who utilize antenatal services in a primary health center (PHC).Materials and Methods: A retrospective analysis was carried out over a six-month period by reviewing the mother and child protection cards maintained at the PHC’s Maternal and Child Health Center. During the study period, 950 pregnant women were registered, of whom 793 were included in the study based on the completeness of the records. Data analysis was performed using the licensed Statistical Package for the Social Sciences (SPSS) software version 21.0.Results: The prevalence of high-risk pregnancy among the antenatal women was 272 (34.3%) with 95% CI [31.1–37.7]. Of the 272 women, 240 (88.2%) had a single high-risk factor, while 32 (11.8%) had more than one high-risk factor. The major factor contributing to high-risk pregnancy was hypothyroidism (43.7% with 95% CI [37.9–49.6]), followed by a previous lower segment Caesarean section (LSCS) (19.1%).Conclusion: The study found that the prevalence of high-risk pregnancies was 34.3% in this rural setting. The majority of high-risk pregnancies were due to hypothyroidism, followed by more than one previous LSCS or abortion. Further research is required to track high-risk pregnancy outcomes and investigate the newborn thyroid profile of women with hypothyroidism.