Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study

Background & Objectives Non-pharmacological interventions (NPI) were crucial in curbing the initial COVID-19 pandemic waves, but compliance was difficult. The primary aim of this study was to assess the changes in compliance with NPIs in healthcare settings using Artificial intelligence (AI) and examine the barriers and facilitators of using AI systems in healthcare. Methods A pre-post-intervention study was conducted in a north-Indian hospital between April and July 2022. YOLO-V5 and 3D Cartesian distance algorithm-based AI modules were used to ascertain compliance through several parameters like confidence threshold, intersection-over-union threshold, image size, distance threshold (6 feet), and 3D Euclidean Distance estimation. Validation was done by evaluating model performance on a labelled test dataset, and accuracy was 91.3 per cent. Interventions included daily sensitization and health education for the hospital staff and visitors, display of information, education and communication (IEC) materials, and administrative surveillance. In-depth interviews were conducted with the stakeholders to assess the feasibility issues. Flagged events during the three phases were compared using One-way ANOVA tests in SPSS. Results Higher social distancing (SD) compliance events were flagged by the module in the intervention phase compared to the pre-intervention and post-intervention phases (P<0.05). Mask non-compliance was significantly lower (P <0.05) in the pre-intervention phase and highest in the post-intervention phase, with varied differences between different intervention phases in the registration hall and medicine out-patient department (OPD). The modules' data safety, transfer, and cost were the most common concerns. Interpretation & conclusions AI can supplement our efforts against the pandemic and offer indispensable help with minimal feasibility issues that can be resolved through adequate sensitization and training.

Impact of Social Restrictions During COVID-19 on the Aquatic Levels of Antimicrobials and Other Drugs in Delhi

The relative contribution of factors responsible for the environmental exposure of active pharmaceutical ingredients (APIs) is of interest for appropriate remedial measures. This study was carried out to evaluate the post-lockdown levels of APIs in water resources, in comparison to our previously published study from 2016. The environmental levels of 28 drugs from different classes were analyzed in surface water (Yamuna River), aquifers, and leachate samples collected from 26 locations in Delhi-NCR using the previously validated liquid chromatography-mass spectrometry (LC-MS/MS) methods. In addition, the prevalence of antimicrobial resistance in coliforms isolated from targeted surface water samples was also studied. This study revealed that more than 90% of APIs, including antibiotics, decreased drastically in both surface water and aquifers compared to our previous data. Selected samples subjected to antimicrobial resistance (AMR) analysis revealed the presence of cephalosporin-resistant coliform bacteria. Tracing cephalosporins in the surface and drain water samples revealed the presence of ceftriaxone in the drain and water samples from Yamuna River. Higher levels of ceftriaxone in landfill leachate were also found, which were found to be associated with coliform resistance and indicate the un-segregated disposal of medical waste into landfills. Social restrictions enforced due to COVID-19 resulted in a drastic decrease in antimicrobials and other APIs in aquatic water resources. Increased ceftriaxone and cephalosporin resistance was seen in coliform from surface water and drain, indicating the possibility of hospital waste and treatment-related drugs entering Yamuna River. Enforcement of the regulations for the safe disposal of antibiotics at hospitals and preliminary disinfection of hospital sewage before its inflow into common drains might help minimize the spread of antibiotic resistance in the environment.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights

BACKGROUND

The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored.

OBJECTIVE

This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies.

METHODS

Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights.

RESULTS

The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events.

CONCLUSIONS

This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Systematic review and meta-analysis on the impact of COVID-19 related restrictions on air quality in low- and middle-income countries

BACKGROUND

Low- and middle-income countries (LMIC) are disproportionately affected by air pollution and its health burden, representing a global inequity. The COVID-19 pandemic provided a unique opportunity to investigate the impact of unprecedented lockdown measures on air pollutant concentrations globally. We aim to quantify air pollutant concentration changes across LMIC settings as a result of COVID-19 restrictions.

METHODS

Searches for this systematic review and meta-analysis were carried out across five databases on 30th March 2022; MEDLINE, Embase, Web of Science, Scopus and Transport Research Information Documentation. Modelling and observational studies were included, as long as the estimates reflected city or town level data and were taken exclusively in pre-lockdown and lockdown periods. Mean percentage changes per pollutant were calculated and meta-analyses were carried out to calculate mean difference in measured ground-level observed concentrations for each pollutant (PROSPERO CRD42022326924).

FINDINGS

Of the 2982 manuscripts from initial searches, 256 manuscripts were included providing 3818 percentage changes of all pollutants. No studies included any countries from Sub-Saharan Africa and 34 % and 39.4 % of studies were from China and India, respectively. There was a mean percentage change of -37.4 %, -21.7 %, -54.6 %, -39.1 %, -48.9 %, 16.9 %, -34.9 %, -30.6 % and - 14.7 % for black carbon (BC), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), ozone (O3), particulate matter 10 (PM10) and 2.5 (PM2.5) and sulphur dioxide (SO2), respectively. Meta-analysis included 100 manuscripts, providing 908 mean concentration differences, which showed significant reduction in mean concentration in all study settings for BC (-0.46 μg/m3, PI -0.85; -0.08), CO (-0.25 mg/m3, PI -0.44; -0.03), NO2 (-19.41 μg/m3, PI -31.14; -7.68) and NOx (-22.32 μg/m3, PI -40.94; -3.70).

INTERPRETATION

The findings of this systematic review and meta-analysis quantify and confirm the trends reported across the globe in air pollutant concentration, including increases in O3. Despite the majority of global urban growth occurring in LMIC, there are distinct geographical gaps in air pollution data and, where it is available, differing approaches to analysis and reporting.

Beyond SARS-CoV-2: epidemiological surveillance of respiratory viruses in Jalisco, Mexico

Introduction

Respiratory viral infections represent a significant global health burden. Historically, influenza, rhinovirus, respiratory syncytial virus, and adenovirus have been the prevalent viruses; however, the landscape shifted with the widespread emergence of SARS-CoV-2. The aim of this study is to present a comprehensive epidemiological analysis of viral respiratory infections in Jalisco, Mexico.

Methods

Data encompassing individuals with flu-like symptoms from July 2021 to February 2023 was scrutinized for viral diagnosis through PCR multiplex. The effect of social mobility on the increase in respiratory viral diagnosis infection was considered to estimate its impact. Additionally, sequences of respiratory viruses stored in public databases were retrieved to ascertain the phylogenetic classification of previously reported viruses in Mexico.

Results

SARS-CoV-2 was the most detected virus (n = 5,703; 92.2%), followed by influenza (n = 479; 7.78%). These viruses were also found as the most common co-infection (n = 11; 50%), and for those with influenza, a higher incidence of severe disease was reported (n = 122; 90.4%; p < 0.001). Regarding comorbidities and unhealthy habits, smoking was found to be a risk factor for influenza infection but a protective factor for SARS-CoV-2 (OR = 2.62; IC 95%: 1.66-4.13; OR = 0.65; IC 95%: 0.45-0.94), respectively. Furthermore, our findings revealed a direct correlation between mobility and the prevalence of influenza infection (0.214; p < 0.001).

Discussion

The study presents evidence of respiratory virus reemergence and prevalence during the social reactivation, facilitating future preventive measures.

Factors Contributing to the Change in Overweight/Obesity Prevalence Among Indian Adults: A multivariate decomposition analysis of data from the National Family Health Surveys

INTRODUCTION

Concerns over the escalating burden of non-communicable diseases call for the redressal of behavioral risk factors like increased body mass index. Most studies have failed to quantify the contribution of socio-demographic characteristics in a linear trend. The present study aims to estimate the current prevalence of overweight and obesity in Indian adults and the contribution of different socio-demographic factors to the increasing prevalence.

METHODS

We carried out a secondary data analysis of two National Family Health Survey (NFHS) rounds. The final sample includes 558,122 women and 84,477 men from round 4, and 574,099 women and 74,761 men were included from round 5, using a multi-stage stratified random sampling approach. Overweight/obesity was our primary dependent variable. Weighted bivariate analysis was used to ascertain the prevalence, and the adjusted odds ratios were computed to ascertain the potential predictors. The contribution of different factors towards rising burden over two time points was estimated using multivariate decomposition analysis for non-linear response models.

RESULTS

Overall weighted prevalence of overweight and obesity in males and females per NFHS-5 was 44.02% and 41.16%, respectively, compared to 37.71% and 36.14% in NFHS-4. Decomposition analyses depict that the proportion of obesity increased by 6.37% and 5.10% points among men and women, respectively, over the two rounds. Compositional differences of participants (endowment) attributed to 16.54 and 49.90% differences, and the difference in coefficient or effect accounted for 83.46 and 50.10%, respectively, of the increase in the prevalence. The most significant factors contributing to increased prevalence were age, improving socio-economic status, smoking, unclean cooking fuel, and diabetes.

CONCLUSIONS

The incremental rise in such a short period, mainly attributed to the effect of socio-demographic variables, is concerning. Policy interventions should prioritize health advocacy programs and aggressively target behavioral modifications while preparing the health systems to manage the people living with obesity.

The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021

Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence.

Changes in mobility patterns during the COVID-19 pandemic in Zambia: Implications for the effectiveness of NPIs in Sub-Saharan Africa

The COVID-19 pandemic has impacted many facets of human behavior, including human mobility partially driven by the implementation of non-pharmaceutical interventions (NPIs) such as stay at home orders, travel restrictions, and workplace and school closures. Given the importance of human mobility in the transmission of SARS-CoV-2, there have been an increase in analyses of mobility data to understand the COVID-19 pandemic to date. However, despite an abundance of these analyses, few have focused on Sub-Saharan Africa (SSA). Here, we use mobile phone calling data to provide a spatially refined analysis of sub-national human mobility patterns during the COVID-19 pandemic from March 2020-July 2021 in Zambia using transmission and mobility models. Overall, among highly trafficked intra-province routes, mobility decreased up to 52% during the time of the strictest NPIs (March-May 2020) compared to baseline. However, despite dips in mobility during the first wave of COVID-19 cases, mobility returned to baseline levels and did not drop again suggesting COVID-19 cases did not influence mobility in subsequent waves.

Nowcasting tourist nights spent using innovative human mobility data

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

INTRODUCTION

Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs.

METHODS

Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R₀), daily reproduction number (R_e), and case doubling time. Based on pre-restriction and Phase 1 R₀, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis.

RESULTS

The estimated median R₀ during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83).

CONCLUSION

Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

The lockdown and vaccination distribution in Thailand's COVID-19 epidemic: A model study

Background

Several countries used varied degrees of social isolation measures in response to the COVID-19 outbreak. In 2021, the lockdown in Thailand began on July 20 and lasted for the following six weeks. The lockdown has extremely detrimental effects on the economy and society, even though it may reduce the number of COVID-19 instances. Our goals are to assess the impact of the lockdown policy, the commencement time of lockdown, and the vaccination rate on the number of COVID-19 cases in Thailand in 2021.

Methods

We modeled the dynamics of COVID-19 in Thailand throughout 2021 using the SEIR model. The Google Mobility Index, vaccine distribution rate, and lockdown were added to the model. The Google Mobility Index represents the movement of individuals during a pandemic and shows how people react to lockdown. The model also examines the effect of vaccination rate on the incidence of COVID-19.

Results

The modeling approach demonstrates that a 6-week lockdown decreases the incidence number of COVID-19 by approximately 15.49-18.17%, depending on the timing of the lockdown compared to a non-lockdown scenario. An increasing vaccination rate potentially reduce the incidence number of COVID-19 by 5.12-18.35% without launching a lockdown.

Conclusion

Lockdowns can be an effective method to slow down the spread of COVID-19 when the vaccination program is not fully functional. When the vaccines are easily accessible on a large scale, the lockdown may terminated.

Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study

BACKGROUND

Many nations swiftly designed and executed government policies to contain the rapid rise in COVID-19 cases. Government actions can be broadly segmented as movement and mass gathering restrictions (such as travel restrictions and lockdown), public awareness (such as face covering and hand washing), emergency health care investment, and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government, University of Oxford, tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using the Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the COVID-19 pandemic.

OBJECTIVE

This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of COVID-19. We expect a significant inverse relationship between policy indices (CHI and SI) and COVID-19 severity indices (morbidity and mortality).

METHODS

In this ecological study, we analyzed data from 2 publicly available data sources released between March 2020 and October 2021: the Oxford Covid-19 Government Response Tracker and the World Health Organization. We used autoregressive integrated moving average (ARIMA) and seasonal ARIMA to model the data. The performance of different models was assessed using a combination of evaluation criteria: adjusted R², root mean square error, and Bayesian information criteria.

RESULTS

implementation of policies by the government to contain the COVID-19 crises resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell, they were consistently higher at values of >80% points. During the initial investigation, we found that cases per million (CPM) and deaths per million (DPM) followed the same trend. However, the final CPM and DPM models were seasonal ARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. This study does not support the hypothesis that COVID-19 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI).

CONCLUSIONS

Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. Twenty-first-century problems thus demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage, and data communication. Thus, digital epidemiology can and should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation.

Characterizing COVID-19 waves in urban and rural districts of India

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.

Client satisfaction with telemedicine services during COVID-19 pandemic: A cross-sectional survey from a teaching institute of North India

Introduction

Telemedicine has emerged as an essential interface between health care providers and patients during the pandemic. The present study was done to assess this technology's level of acceptance and satisfaction amongst the patients.

Methods

We did a retrospective study amongst patients >18 years (n = 300) who had availed telemedicine services in different departments of a tertiary care hospital between May and August 2020. The patients were interviewed telephonically using a pre-tested semi-structured tool that collected information about the socio-demographic and clinical characteristics of the patients, and satisfaction was measured on a 5-point Likert Scale.

Results

Fifty-five percent patients received teleconsultation via a telephone call, while the others preferred video calling services on WhatsApp messenger. Overall, more than 97% of the clients depicted satisfaction with the telemedicine services in three major domains: registration/appointment services, consultation with the doctor and post-consultation services. Some of the common feedback included difficulty in getting medicine using the scanned copy of prescription slip generated by the hospital, problems faced in reimbursement of the bills, long waiting period, and poor quality of video calls due to slow internet.

Conclusion

Telemedicine proved to be an efficient means of communication for many patients during the pandemic. Though patient satisfaction was high with the services received by them, timely assessment of the problems encountered in the implementation of telemedicine services will help evolve the services not just during the pandemic but even after that.

Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data

The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.

Numerical Simulation to Predict COVID-19 Cases in Punjab

Coronavirus disease 2019 is a novel disease caused by a newly identified virus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). India recorded its first case of COVID-19 on 30 January 2020. This work is an attempt to calculate the number of COVID-19 cases in Punjab by solving a partial differential equation using the modified cubic B-spline function and differential quadrature method. The real data of COVID-19 cases and Google Community Mobility Reports of Punjab districts were used to verify the numerical simulation of the model. The Google mobility data reflect the changes in social behavior in real time and therefore are an important factor in analyzing the spread of COVID-19 and the corresponding precautionary measures. To investigate the cross-border transmission of COVID-19 between the 23 districts of Punjab with an analysis of human activities as a factor, the 23 districts were divided into five regions. This paper is aimed at demonstrating the predictive ability of the model.

Mask wearing in community settings reduces SARS-CoV-2 transmission

The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n= 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing.

Effect of restrictions imposed due to COVID-19 pandemic on the antenatal care and pregnancy outcomes: a prospective observational study from rural North India

OBJECTIVES

To assess the difficulties faced by the pregnant women in seeking appropriate antenatal care due to the restrictions imposed during the COVID-19 pandemic; assess the difficulties encountered during delivery and postpartum period; the suitability of the teleconsultation services offered; effect of COVID-19 infection on pregnancy outcomes and the effect of restrictions on the nutrition profile of the pregnant women.

DESIGN

Prospective observational study.

SETTING AND PARTICIPANTS

We included 1374 pregnant women from the rural areas of three districts of Punjab, India registered at government health centres before the implementation of lockdown due to the COVID-19 pandemic on 24 March 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was the difficulties faced by the women during their pregnancies due to restrictions imposed during the lockdown. The secondary outcomes included the effect of COVID-19 infections on pregnancy outcomes, satisfaction from the telemedicine services and restrictions on the nutrition profile of the pregnant women.

RESULTS

One-third of the women (38.4%) considered their last pregnancy unplanned. Women faced difficulties due to the restrictions in getting adequate nutrition (76.5%), accessing transportation facilities (35.4%), consultations from doctors (22.4%) or getting an ultrasonography scan (48.7%). One-fifth (21.9%) of women could not access safe abortion services. Only 3.6% of respondents ever took any teleconsultation services offered by the government. Most of them felt unsatisfied compared with routine visits (77.5%). COVID-19-infected women were primarily asymptomatic (76.1%), but there was a high incidence of preterm birth (42.8%). Frontline workers could visit 64.3% of the women in the postpartum period despite restrictions.

CONCLUSIONS

Lockdown compromised the antenatal care in our study area while the frontline workers attempted to minimise the inconvenience. Telemedicine services did not prove to be of many benefits to pregnant women and should only work as a supplement to the existing protocols of antenatal care.

It is complicated: Potential short- and long-term impact of coronavirus disease 2019 (COVID-19) on antimicrobial resistance—An expert review

As of December 2021, the coronavirus disease 2019 (COVID-19) pandemic has claimed millions of deaths and caused disruptions in health systems around the world. The short- and long-term effects of COVID-19 on antimicrobial resistance (AMR), which was already a global threat before the pandemic, are manifold and complex. In this expert review, we summarize how COVID-19 might be affecting AMR in the short term (by influencing the key determinants antibiotic use, infection control practices and international/local mobility) and which additional factors might play a role in the long term. Whereas reduced outpatient antibiotic use in high-income countries, increased awareness for hand hygiene, and reduced mobility have likely mitigated the emergence and spread of AMR in the short term, factors such as overuse of antibiotics in COVID-19 patients, shortage of personal protective equipment, lack of qualified healthcare staff, and patient overcrowding have presumably facilitated its propagation. Unsurprisingly, international and national AMR surveillance data for 2020 show ambiguous trends. Although disruptions in antibiotic stewardship programs, AMR surveillance and research might promote the spread of AMR, other developments could prove beneficial to the cause in the long term. These factors include the increased public awareness for infectious diseases and infection control issues, the strengthening of the One Health perspective as outlined by the Centers for Disease Control and Prevention, and the unprecedented number of international research collaborations and platforms. These factors could even serve as leverage and provide opportunities to better combat AMR in the future.

Empirical evidence of the impact of mobility on property crimes during the first two waves of the COVID-19 pandemic

This paper seeks to evaluate the impact of the removal of restrictions (partial and complete) imposed during COVID-19-induced lockdowns on property offences such as robbery, burglary, and theft during the milder wave one and the more severe wave two of the pandemic in 2020 and 2021, respectively. Using 10-year data of the daily counts of crimes, the authors adopt an auto-regressive neural networks method to make counterfactual predictions of crimes, representing a scenario without the pandemic-induced lockdowns. The difference between the actual and forecast is the causal impact of the lockdown in all phases. Further, the research uses Google Mobility Community Reports to measure mobility. The analysis has been done at two levels: first, for the state of Tamil Nadu, which has a sizeable rural landscape, and second for Chennai, the largest metropolitan city with an urban populace. During the pandemic-induced lockdown in wave one, there was a steep decline in the incidence of property offences. On removing restrictions, the cases soared above the counterfactual predicted counts. In wave two, despite the higher severity and fatality in the COVID-19 pandemic, a similar trend of fall and rise in property cases was observed. However, the drop in mobility was less substantial, and the increase in the magnitude of property offences was more significant in wave two than in wave one. The overall trend of fluctuations is related to mobility during various phases of restrictions in the pandemic. When most curbs were removed, there was a surge in robberies in Tamil Nadu and Chennai after adjusting for mobility. This trend highlights the effective increase in crime due to pandemic-related economic and social consequences. Further, the research enables law enforcement to strengthen preventive crime work in similar situations, when most curbs are removed after a pandemic or other unanticipated scenarios.