Dataset Information

Interplay of weather variables in triggering the transmission of SARS-CoV-2 infection in Asia

ABSTRACT: Weather variables are one of the crucial factors affecting respiratory infectious diseases; however, the effect of weather variables on the coronavirus disease 2019 (COVID-19) is still inconclusive and varies in different regions. The present study investigated the effects of weather variables (maximum temperature, MT; relative humidity, RH; wind speed, WS; precipitation, PR; and dew point, DP) on daily infection and death cases in three lockdown phases in Asia as of November 1, 2020. Generalized additive lag model was used to analyze the risk associated with weather variables, with confounders like median age of the national population, population density, country and lockdown phases. Our findings revealed that during lockdown phases all five weather variables show association with daily confirmed, and death cases. On the other hand, PR (pre-lockdown phase) and DP (lockdown phase) showed positive association with both daily confirmed and death cases. Throughout the three lockdown phases MT, RH and PR showed strong positive associations with daily confimed/death cases. A lag period of 0–4-days possess higher risk of infection and death due to the varied ratios of different weather variables. The relative risk indicated that the infection and mortality risk was higher in India as compared to the rest of the countries. Here, unique combination of weather variables together with higher population density makes this region as one of the hotspots for COVID-19. This shows that the COVID-19 pandemic may be suppressed or enhanced with combination of different weather conditions together with factors like population density and median age of the country, which shall be useful for better implementation of health policies and further preparedness in Asia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42398-021-00176-8.

SUBMITTER: Dalal A

PROVIDER: S-EPMC8126599 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundUnderstanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution.MethodsIn this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission.FindingsOur dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations.InterpretationWarmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects.FundingNone.

Project description:BackgroundSARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19.ObjectiveThe goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully.MethodsUsing a longitudinal trend analysis study design, we extracted 60 days of COVID-19-related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsCOVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight.ConclusionsThe rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

Dataset Information

Interplay of weather variables in triggering the transmission of SARS-CoV-2 infection in Asia

Supplementary Information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets