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Human mobility impacts on the surging incidence of COVID‐19 in India

ABSTRACT: Abstract Human mobility triggers how fast and where infectious diseases spread and modelling community flows helps assess the impact of social distancing policies and advance our understanding of community behaviour in such circumstances. This study investigated the relationship between human mobility and the surging incidence of COVID‐19 in India. We performed a generalised estimating equation with a Poisson log‐linear model to analyse the daily mobility rate and new cases of COVID‐19 between 14 March and 11 September 2020. We found that mobility to grocery and retail locations was significantly associated (p < 0.01) with the incidence of COVID‐19, these being crowded and unorganised in most parts of India. In contrast, visits to parks, workplaces, and transit stations did not considerably affect the changing COVID‐19 cases over time. In particular, workplaces equipped with social distancing protocols or low‐density open spaces are much less susceptible to the spread of the virus. These findings suggest that human mobility data, geographic information, and health geography modelling have significant potential to inform strategic decision‐making during pandemics because they provide actionable knowledge of when and where communities might be exposed to the disease. This study conducts an advanced health geography modelling with GEE Poisson log‐linear method to investigate the relationship between daily human mobility and surging COVID‐19 cases in India. The results show upward mobility in which locations (grocery and shopping centres, workplaces, parks, transit stations, and residential areas) have a significant positive association with the alarming increase in COVID‐19 cases with time. The spatial‐temporal identification of potential risk areas vis‐à‐vis community mobility supports evidenceàbased public health decisionàmaking and social distancing policies to contain the COVIDà19 pandemic.

SUBMITTER: Praharaj S

PROVIDER: S-EPMC8652497 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Coronavirus disease (COVID-19) is a novel public health problem threatening the whole world. As an upshot, countrywide lockdown due to COVID-19 pandemic has been supportive of changing community mobility trends of various place categories including retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential in India. Objective:To analyze the impact of lockdown for COVID-19 on community mobility using spatial time-series change over different states and union territories (UTs) of India. Data & Methods:This study has been organized based on states & UTs wise time-series data of the daily percentage of change of community mobility from baseline in India, collected from 15th February to 30th April 2020. Conditional formatting techniques, time-series trends plotting method, spatial inverse distance weighted (IDW) interpolation mapping techniques have been employed to show pre and post lockdown mobility trends due to COVID-19 i.e. to fulfill the objective. Results:Across India, retail and recreation, grocery and pharmacy, visits to parks, transit stations, and workplaces mobility dropped by -73.4%, -51.2%, -46.3%, -66% and -56.7% respectively. Visits to residential places mobility increased by 23.8% as people mostly stayed home during the lockdown. The COVID-19 lockdown started on 24 March 2020 and just gone one day (March 25, 2020) of the beginning of lockdown, there have a decreased in percentage (-70.51% in retail and recreation mobility), (-64.26% in grocery and pharmacy mobility), (-46.17% in parks mobility), (-65.6% in transit stations mobility), (-60.03% in workplaces mobility) from baseline in compared to the pre-lockdown period and residential mobility has been increased in percentage (26.32%) from baseline due to people stayed home during the lockdown for COVID-19 pandemic in India. Conclusion:Study figures out mobility trends over time during pre-lockdown and after lockdown period across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential, which can be used in public health strategies to drop the spread of COVID-19.

Project description:Since the World Health Organization has declared the current outbreak of the novel coronavirus (COVID-19) a global pandemic, some have been anticipating that the mitigation could happen in the summer like seasonal influenza, while medical solutions are still in a slow progress. Experimental studies have revealed a few evidences that coronavirus decayed quickly under the exposure of heat and humidity. This study aims to carry out an epidemiological investigation to establish the association between meteorological factors and COVID-19 in high risk areas of the United States (U.S.). We analyzed daily new confirmed cases of COVID-19 and seven meteorological measures in top 50 U.S. counties with the most accumulative confirmed cases from March 22, 2020 to April 22, 2020. Our analyses indicate that each meteorological factor and COVID-19 more likely have a nonlinear association rather than a linear association over the wide ranges of temperature, relative humidity, and precipitation observed. Average temperature, minimum relative humidity, and precipitation were better predictors to address the meteorological impact on COVID-19. By including all the three meteorological factors in the same model with their lagged effects up to 3 days, the overall impact of the average temperature on COVID-19 was found to peak at 68.45 °F and decrease at higher degrees, though the overall relative risk percentage (RR %) reduction did not become significantly negative up to 85 °F. There was a generally downward trend of RR % with the increase of minimum relative humidity; nonetheless, the trend reversed when the minimum relative humidity exceeded 91.42%. The overall RR % of COVID-19 climbed to the highest level of 232.07% (95% confidence interval?=?199.77, 267.85) with 1.60 inches of precipitation, and then started to decrease. When precipitation exceeded 1.85 inches, its impact on COVID-19 became significantly negative. Our findings alert people to better have self-protection during the pandemic rather than expecting that the natural environment can curb coronavirus for human beings.

Project description:BackgroundContact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be.MethodsWe analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted.ResultsModels using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998.ConclusionsUsing anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

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Human mobility impacts on the surging incidence of COVID‐19 in India

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