Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

The impact of meteorological factors on the spread of COVID-19.

Background: Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity. Aim: This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19). Patients and Methods: This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1st Regional Directorate. Results: The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality. Conclusion: Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study.

BACKGROUND: Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs). OBJECTIVE: To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs. METHODS: We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model. RESULTS: There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites. CONCLUSION: Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

Determining the Relationship of Meteorological Factors and Severe Pediatric Respiratory Syncytial Virus (RSV) Infection in Central Peninsular Malaysia.

Respiratory syncytial virus (RSV) is the most common pathogen causing viral respiratory tract infections among younger children worldwide. The influence of meteorological factors on RSV seasonal activity is well-established for temperate countries; however, in subtropical countries such as Malaysia, relatively stable temperate climates do not clearly support this trend, and the available data are contradictory. Better understanding of meteorological factors and seasonality of RSV will allow effective strategic health management relating to RSV infection, particularly immunoprophylaxis of high-risk infants with palivizumab. Retrospectively, from 2017 to 2021, we examined the association between various meteorological factors (rainfall, rainy days, temperature, and relative humidity) and the incidence of RSV in children aged less than 12 years in Kuala Lumpur, Malaysia. RSV activity peaked in two periods (July to August and October to December), which was significantly correlated with the lowest rainfall (p < 0.007) and number of rainy days (p < 0.005). RSV prevalence was also positively associated with temperature (p < 0.006) and inversely associated with relative humidity (p < 0.006). Based on our findings, we recommend that immunoprophylaxis with palivizumab be administered in children aged less than 2 years where transmission of RSV is postulated to be the highest after the end of two monsoon seasons.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave.

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis.

BACKGROUND: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

BACKGROUND: Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction. METHODS: We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). RESULTS: Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper. CONCLUSIONS: The experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021.

We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m3. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.

[The influence of meteorological factors on SARS-CoV-2 transmission: evidence from laboratory and epidemiological studies].

SARS-CoV-2 has infected more than 600 million people worldwide and caused more than 6 million deaths. The emerging novel variants have made the epidemic rebound in many places. Meteorological factors can affect the epidemic spread by changing virus activity, transmission dynamic parameters and host susceptibility. This paper systematically analyzed the currently available laboratory and epidemiological studies on the association between the meteorological factors and COVID-19 incidence, in order to provide scientific evidence for future epidemic control and prevention, as well as developing early warning system.

The relationship among air pollution, meteorological factors and COVID-19 in the Brussels Capital Region.

In great metropoles, there is a need for a better understanding of the spread of COVID-19 in an outdoor context with environmental parameters. Many studies on this topic have been carried out worldwide. However, there is conflicting evidence regarding the influence of environmental variables on the transmission, hospitalizations and deaths from COVID-19, even though there are plausible scientific explanations that support this, especially air quality and meteorological factors. Different urban contexts, methodological approaches and even the limitations of ecological studies are some possible explanations for this issue. That is why methodological experimentations in different regions of the world are important so that scientific knowledge can advance in this aspect. This research analyses the relationship between air pollution, meteorological factors and COVID-19 in the Brussels Capital Region. We use a data mining approach that is capable of extracting patterns in large databases with diverse taxonomies. Data on air pollution, meteorological, and epidemiological variables were processed in time series for the multivariate analysis and the classification based on association. The environmental variables associated with COVID-19-related deaths, cases and hospitalization were PM2.5, O3, NO2, black carbon, radiation, air pressure, wind speed, dew point, temperature and precipitation. These environmental variables combined with epidemiological factors were able to predict intervals of hospitalization, cases and deaths from COVID-19. These findings confirm the influence of meteorological and air quality variables in the Brussels region on deaths and cases of COVID-19 and can guide public policies and provide useful insights for high-level governmental decision-making concerning COVID-19. However, it is necessary to consider intrinsic elements of this study that may have influenced our results, such as the use of air quality aggregated data, ecological fallacy, focus on acute effects in the time-series study, the underreporting of COVID-19, and the lack of behavioral factors.

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers.

COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (ß = 11.91, 95% CI: 4.77, 19.05) and India (ß = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (ß = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (ß = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

The Temporal Lagged Relationship Between Meteorological Factors and Scrub Typhus With the Distributed Lag Non-linear Model in Rural Southwest China.

Background: Meteorological factors can affect the emergence of scrub typhus for a period lasting days to weeks after their occurrence. Furthermore, the relationship between meteorological factors and scrub typhus is complicated because of lagged and non-linear patterns. Investigating the lagged correlation patterns between meteorological variables and scrub typhus may promote an understanding of this association and be beneficial for preventing disease outbreaks. Methods: We extracted data on scrub typhus cases in rural areas of Panzhihua in Southwest China every week from 2008 to 2017 from the China Information System for Disease Control and Prevention. The distributed lag non-linear model (DLNM) was used to study the temporal lagged correlation between weekly meteorological factors and weekly scrub typhus. Results: There were obvious lagged associations between some weather factors (rainfall, relative humidity, and air temperature) and scrub typhus with the same overall effect trend, an inverse-U shape; moreover, different meteorological factors had different significant delayed contributions compared with reference values in many cases. In addition, at the same lag time, the relative risk increased with the increase of exposure level for all weather variables when presenting a positive association. Conclusions: The results found that different meteorological factors have different patterns and magnitudes for the lagged correlation between weather factors and scrub typhus. The lag shape and association for meteorological information is applicable for developing an early warning system for scrub typhus.

Effect of ambient air pollution and meteorological factors on the potential transmission of COVID-19 in Turkey.

There is a need to improve the understanding of air quality parameters and meteorological conditions on the transmission of SARS-CoV-2 in different regions of the world. In this preliminary study, we explore the relationship between short-term air quality (nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM2.5, PM10)) exposure, temperature, humidity, and wind speed on SARS-CoV-2 transmission in 41 cities of Turkey with reported weekly cases from February 8 to April 2, 2021. Both linear and non-linear relationships were explored. The nonlinear association between weekly confirmed cases and short-term exposure to predictor factors was investigated using a generalized additive model (GAM). The preliminary results indicate that there was a significant association between humidity and weekly confirmed COVID-19 cases. The cooler temperatures had a positive correlation with the occurrence of new confirmed cases. The low PM2.5 concentrations had a negative correlation with the number of new cases, while reducing SO2 concentrations may help decrease the number of new cases. This is the first study investigating the relationship between measured air pollutants, meteorological factors, and the number of weekly confirmed COVID-19 cases across Turkey. There are several limitations of the presented study, however, the preliminary results show that there is a need to understand the impacts of regional air quality parameters and meteorological factors on the transmission of the virus.

The Impact of Meteorological Factors and Air Pollutants on Acute Coronary Syndrome.

PURPOSE OF REVIEW: Several studies have found that air pollution and climate change can have an impact on acute coronary syndromes (ACS), the leading cause of death worldwide. We synthesized the latest information about the impact of air pollution and climate change on ACS, the latest data about the pathophysiological mechanisms of meteorological factors and atmospheric pollutants on atherosclerotic disease, and an overall image of air pollution and coronary heart disease in the context of the COVID-19 pandemic. RECENT FINDINGS: The variation of meteorological factors in different seasons increased the risk of ACS. Both the increase and the decrease in apparent temperature were found to be risk factors for ACS admissions. It was also demonstrated that exposure to high concentrations of air pollutants, especially particulate matter, increased cardiovascular morbidity and mortality. Climate change as well as increased emissions of air pollutants have a major impact on ACS. The industrialization era and the growing population cause a constant increase in air pollution worldwide. Thus, the number of ACS favored by air pollution and the variations in meteorological factors is expected to increase dramatically in the next few years.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland.

Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.