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Winalai C, Anupong S, Modchang C, Chadsuthi S. LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach. Heliyon 2024; 10:e30319. [PMID: 38711630 PMCID: PMC11070856 DOI: 10.1016/j.heliyon.2024.e30319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024] Open
Abstract
The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.
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Affiliation(s)
- Chanidapa Winalai
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
| | - Suparinthon Anupong
- Department of Chemistry, Mahidol Wittayanusorn School (MWIT), Salaya, Nakhon Pathom 73170, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- Centre of Excellence in Mathematics, CHE, Bangkok 10400, Thailand
- Thailand Center of Excellence in Physics, CHE, 328 Si Ayutthaya Road, Bangkok 10400, Thailand
| | - Sudarat Chadsuthi
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
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Haq FU, Abduljaleel Y, Ahmad I. Effect of temperature on fast transmission of COVID-19 in low per capita GDP Asian countries. Sci Rep 2023; 13:21165. [PMID: 38036656 PMCID: PMC10689760 DOI: 10.1038/s41598-023-48587-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023] Open
Abstract
An abrupt outbreak of COVID-19 caused enormous global concerns. Although all countries around the world are severely affected, developing Asian countries faced more difficulties due to their low per capita GDP. The temperature was considered a leading variable in spreading viral diseases, including COVID-19. The present study aimed to assess the relationship between temperature and the spread of COVID-19, with a focus on developing Asian countries. In a few Asian countries, COVID-19 spread rapidly in the summer, while in some countries, there is an increase in winter. A linear correlation was developed between COVID-19 cases/deaths and temperature for the selected countries, which were very weak. A coefficient of determination of 0.334 and 0.365 was observed between cases and average monthly max/min temperatures. A correlation of R2 = 0.307 and 0.382 was found between deaths and average max/min monthly temperatures, respectively. There is no scientific reason to assume that COVID-19 is more dominant at low than high temperatures. Therefore, it is believed that the results may be helpful for the health department and decision-makers to understand the fast spread of COVID-19.
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Affiliation(s)
- Faraz Ul Haq
- Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
- Department of Civil Engineering, University of Memphis, Memphis, TN, 38152, USA.
| | - Yasir Abduljaleel
- Department of Civil and Environmental Engineering, Washington State University, Richland, WA, 99354, USA
| | - Ijaz Ahmad
- Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan
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Fu Y, Wang W. Association between provincial sunshine duration and mortality rates in China: Panel data study. Heliyon 2023; 9:e15862. [PMID: 37215780 PMCID: PMC10199197 DOI: 10.1016/j.heliyon.2023.e15862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 03/09/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Background mortality rates are usually influenced by the variations of environmental factors. However, there are few studies on the impact of sunlight duration induced mortality. In this study, we examine provincial level associations between the sunshine duration and crude mortality rates. Methods we use China mortality data from the National Bureau of Statistics of China combined with China census data and data from the China Meteorological Data Service Centre. Annual mortality rates for 31 provinces, autonomous regions, and municipalities in China from 2005 to 19. Data are analyzed at the provincial level by using panel regression methods. The main outcome measures are the mortality rates associated with average daily sunshine duration. Then we perform a series of sentimental analyses. Results the average daily sunshine duration ratio cubed is positively associated with provincial level mortality rates (β = 11.509, 95% confidence interval 1.869 to 21.148). According to this estimate, increasing 2.895 h of additional daily sunshine is associated with an estimated 1.15% increase in the crude mortality rates. A series of sensitivity analyses show a consistent pattern of associations between average daily sunshine duration ratio cubed and mortality rates. Conclusions more sunshine duration is associated with increased mortality rates. While the associations documented cannot be assumed to be causal, they suggest a potential association between increased sunshine duration and increased mortality rates.
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Affiliation(s)
- Yu Fu
- Urban Vocational College of Sichuan, Chengdu, China
| | - Weidong Wang
- Sichuan Cancer Hospital & Institute, Chengdu, China
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Zhai G, Qi J, Zhou W, Wang J. The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2023; 84:103478. [PMID: 36505181 PMCID: PMC9721135 DOI: 10.1016/j.ijdrr.2022.103478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/14/2022] [Accepted: 12/01/2022] [Indexed: 05/11/2023]
Abstract
The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.
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Affiliation(s)
- Guangyu Zhai
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jintao Qi
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Wenjuan Zhou
- Gansu Provincial Hospital, Lanzhou, 730000, China
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Brągoszewska E, Mainka A. Impact of Different Air Pollutants (PM 10, PM 2.5, NO 2, and Bacterial Aerosols) on COVID-19 Cases in Gliwice, Southern Poland. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14181. [PMID: 36361060 PMCID: PMC9655007 DOI: 10.3390/ijerph192114181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/23/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Many studies have shown that air pollution may be closely associated with increased morbidity and mortality due to COVID-19. It has been observed that exposure to air pollution leads to reduced immune response, thereby facilitating viral penetration and replication. In our study, we combined information on confirmed COVID-19 daily new cases (DNCs) in one of the most polluted regions in the European Union (EU) with air-quality monitoring data, including meteorological parameters (temperature, relative humidity, atmospheric pressure, wind speed, and direction) and concentrations of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen oxides (NO and NO2), ozone (O3), and carbon monoxide (CO). Additionally, the relationship between bacterial aerosol (BA) concentration and COVID-19 spread was analyzed. We confirmed a significant positive correlation (p < 0.05) between NO2 concentrations and numbers of confirmed DNCs and observed positive correlations (p < 0.05) between BA concentrations and DNCs, which may point to coronavirus air transmission by surface deposits on bioaerosol particles. In addition, wind direction information was used to show that the highest numbers of DNCs were associated with the dominant wind directions in the region (southern and southwestern parts).
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Affiliation(s)
- Ewa Brągoszewska
- Department of Technologies and Installations for Waste Management, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 18 Konarskiego St., 44-100 Gliwice, Poland
| | - Anna Mainka
- Department of Air Protection, Silesian University of Technology, 22B Konarskiego St., 44-100 Gliwice, Poland
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Correlation between COVID-19 and weather variables: A meta-analysis. Heliyon 2022; 8:e10333. [PMID: 35996423 PMCID: PMC9387066 DOI: 10.1016/j.heliyon.2022.e10333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 06/22/2022] [Accepted: 08/12/2022] [Indexed: 01/09/2023] Open
Abstract
Background COVID-19 has significantly impacted humans worldwide in recent times. Weather variables have a remarkable effect on COVID-19 spread all over the universe. Objectives The aim of this study was to find the correlation between weather variables with COVID-19 cases and COVID-19 deaths. Methods Five electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022. Both fixed-effects and random-effects models were used to calculate pooled correlation and 95% confidence interval (CI) for both effect measures. Included studies heterogeneity was measured by Cochrane chi-square test statistic Q, I2 and τ2. Funnel plot was used to measure publication bias. A Sensitivity analysis was also carried out. Results Total 38 studies were analyzed in this study. The result of this analysis showed a significantly negative impact on COVID-19 fixed effect incidence and weather variables such as temperature (r = -0.113∗∗∗), relative humidity (r = -0.019∗∗∗), precipitation (r = -0.143∗∗∗), air pressure (r = -0.073∗), and sunlight (r = -0.277∗∗∗) and also found positive impact on wind speed (r = 0.076∗∗∗) and dew point (r = 0.115∗∗∗). From this analysis, significant negative impact was also found for COVID-19 fixed effect death and weather variables such as temperature (r = -0.094∗∗∗), wind speed (r = -0.048∗∗), rainfall (r = -0.158∗∗∗), sunlight (r = -0.271∗∗∗) and positive impact for relative humidity (r = 0.059∗∗∗). Conclusion This meta-analysis disclosed significant correlations between weather and COVID-19 cases and deaths. The findings of this analysis would help policymakers and the health professionals to reduce the cases and fatality rate depending on weather forecast techniques and fight this pandemic using restricted assets.
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Su Y, Cheng H, Wang Z, Wang L. Impacts of the COVID-19 lockdown on building energy consumption and indoor environment: A case study in Dalian, China. ENERGY AND BUILDINGS 2022; 263:112055. [PMID: 35370351 PMCID: PMC8959662 DOI: 10.1016/j.enbuild.2022.112055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/12/2022] [Accepted: 03/23/2022] [Indexed: 05/03/2023]
Abstract
Restricting social distancing is an effective means of controlling the COVID-19 pandemic, resulting in a sharp drop in the utilization of commercial buildings. However, the specific changes in the operating parameters are not clear. This study aims to quantify the impact of COVID-19 lockdowns on commercial building energy consumption and the indoor environment, including correlation analysis. A large green commercial building in Dalian, China's only country to experience five lockdowns, has been chosen. We compared the performance during the lockdown to the same period last year. The study found that the first lockdown caused a maximum 63.5% drop in monthly energy consumption, and the second lockdown was 55.2%. The energy consumption per unit area in 2020 dropped by 55.4% compared with 2019. In addition, during the lockdown, the compliance rate of indoor thermal environment increased by 34.7%, and indoor air quality was 9.5%. These findings could partly explain the short-term and far-reaching effects of the lockdown on the operating parameters of large commercial buildings. Humans are likely to coexist with COVID-19 for a long time, and commercial buildings have to adapt to new energy and health demands. Effective management strategies need to be developed.
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Affiliation(s)
- Yuan Su
- School of Architecture & Fine Art, Dalian University of Technology, Dalian 116024, China
| | - Haoyuan Cheng
- School of Architecture & Fine Art, Dalian University of Technology, Dalian 116024, China
| | - Zhe Wang
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, China
| | - Linwei Wang
- China Merchants Shekou Holdings Northeast Corporation, China
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Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19094989. [PMID: 35564384 PMCID: PMC9100265 DOI: 10.3390/ijerph19094989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 01/09/2023]
Abstract
The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO2, 03, SO2, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O3 had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.
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Exposure to PM2.5 and PM10 and COVID-19 Infection Rates and Mortality: a one-year observational study in Poland. Biomed J 2021; 44:S25-S36. [PMID: 34801766 PMCID: PMC8603332 DOI: 10.1016/j.bj.2021.11.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/09/2021] [Accepted: 11/12/2021] [Indexed: 01/26/2023] Open
Abstract
Background Atmospheric contamination, especially particulate matter (PM), can be associated viral infections connected with respiratory failure. Literature data indicates that intensity of SARS-CoV-2 infections worldwide can be associated with PM pollution levels. Objectives The aim of the study was to examine the relationship between atmospheric contamination, measured as PM2.5 and PM10 levels, and the number of COVID-19 cases and related deaths in Poland in a one-year observation study. Methods Number and geographical distribution of COVID-19 incidents and related deaths, as well as PM2.5 and PM10 exposure levels in Poland were obtained from publicly accessible databases. Average monthly values of these parameters for individual provinces were calculated. Multiple regression analysis was performed for the period between March 2020 and February 2021, taking into account average monthly exposure to PM2.5 and PM10, monthly COVID-19 incidence and mortality rates per 100,000 inhabitants and the population density across Polish provinces. Results Only December 2020 the number of new infections was significantly related to the three analyzed factors: PM2.5, population density and the number of laboratory COVID-19 tests (R2 = 0.882). For COVID-19 mortality, a model with all three significant factors: PM10, population density and number of tests was obtained as significant only in November 2020 (R2 = 0.468). Conclusion The distribution of COVID-19 incidents across Poland was independent from annual levels of particulate matter concentration in provinces. Exposure to PM2.5 and PM10 was associated with COVID-19 incidence and mortality in different provinces only in certain months. Other cofactors such as population density and the number of performed COVID-19 tests also corresponded with both COVID-19-related infections and deaths only in certain months. Particulate matter should not be treated as the sole determinant of the spread and severity of the COVID-19 pandemic but its importance in the incidence of infectious diseases should not be forgotten.
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Abstract
Extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, tornadoes, and many others usually occur when there is a change in air mass and the passing of a weather front over a certain region. The climatology of weather fronts is difficult, since they are usually drawn onto maps manually by forecasters; therefore, the data concerning them are limited and the process itself is very subjective in nature. In this article, we propose an objective method for determining the position of weather fronts based on the random forest machine learning technique, digitized fronts from the DWD database, and ERA5 meteorological reanalysis. Several aspects leading to the improvement of scores are presented, such as adding new fields or dates to the training database or using the gradients of fields.
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Gómez-Herrera S, Sartori Jeunon Gontijo E, Enríquez-Delgado SM, Rosa AH. Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach. Int J Hyg Environ Health 2021; 238:113833. [PMID: 34461424 PMCID: PMC8384590 DOI: 10.1016/j.ijheh.2021.113833] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 08/12/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m3) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m3. The results support the implementation of better local control measures based on the particularities of tropical regions.
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Affiliation(s)
- Santiago Gómez-Herrera
- São Paulo State University (UNESP), Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa Vista, CEP: 18087-180, Sorocaba, SP, Brazil
| | - Erik Sartori Jeunon Gontijo
- São Paulo State University (UNESP), Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa Vista, CEP: 18087-180, Sorocaba, SP, Brazil
| | | | - André H Rosa
- São Paulo State University (UNESP), Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa Vista, CEP: 18087-180, Sorocaba, SP, Brazil.
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