Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

Mofijur, M.; Fattah, I.M. Rizwanul; Islam, A. B. M. Saiful; Uddin, Mohy; Rahman, Sabrina; Chowdhury, Mohammad Asaduzzaman; Alam, Asraful; Uddin, Md. Alhaz

doi:10.3390/su12208319

Cited by 31 publications

(19 citation statements)

References 33 publications

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“…This indicates that an increase in either of the mentioned meteorological factors enhances the number of confirmed cases. These results agree with those in Liu et al 41 and Mofijur et al 42 where the mean temperature has positive correlation with the COVID-19 cases. The positive correlation between number of confirmed cases and relative humidity and precipitation can be explained by this fact that in relatively moist air, the virus can easily stick to the surface of the object and reproduce.…”

Section: Resultssupporting

confidence: 93%

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Chen

Karimian

et al. 2021

Sci Rep

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In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China’s non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.

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Section: Resultssupporting

confidence: 93%

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Chen

Karimian

et al. 2021

Sci Rep

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“…While in Bangladesh, all the meteorological factors exhibit a negative impact on the COVID-19 confirmed cases, with air pressure reporting the highest negative impact on the confirmed cases, confirming that an increase in air pressure may lead to a decrease in the number of COVID-19 cases. This finding is partially in line with the results opined by Islam et al (2020) and Mofijur et al (2020) which report humidity and temperature, to have a significant impact on the COVID-19 cases in Bangladesh, respectively. Surprisingly, in contrast to recent studies based on India as a sample (Kumar 2020b), which report a significant impact of meteorological factors, namely, temperature and humidity, our study partially contradicts such findings implying that the meteorological factors exhibit causality, but do not exhibit any significant impact on the COVID-19 confirmed cases in India and Pakistan.…”

Section: Resultssupporting

confidence: 90%

Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia

Jain

Sharma

Goyal³

et al. 2021

Environ Sci Pollut Res

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The pandemic has affected almost 74 million people worldwide as of 17 December 2020. This is the first study that attempts to examine the nexus between the confirmed COVID-19 cases, deaths, meteorological factors, and the air pollutant namely PM2.5 in six South Asian countries, from 1 March 2020 to 30 June 2020, using the advanced econometric techniques that are robust to heterogeneity across nations. Our findings confirm (1) a strong cross-sectional dependence and significant correlation between COVID-19 cases, deaths, meteorological factors, and air pollutant; (2) long-term relationship between all the meteorological variables, air pollutant, and COVID-19 death cases; (3) temperature, air pressure, and humidity exhibit a significant impact on the COVID-19 confirmed cases, while COVID-19 confirmed cases and air pollutant PM2.5 have a statistically significant impact on the COVID-19 death cases. In this way, the conclusion that high temperature and high humidity increase the transmission of the COVID-19 infections can also be applied to the regions with greater transmission rates, where the minimum temperature is mostly over 21°C and humidity ranges around 80% for months. From the findings, it is evident that majority of the meteorological factors and air pollutant PM2.5 exhibit significant negative and positive effects on the number of COVID-19 confirmed cases and death cases in the six countries under study. Air pollutant PM 2.5 provides more particle surface for the virus to stick and get transported longer distances. Hence, higher particulate pollution levels in the air increase COVID-19 transmission in these six South Asian countries. This information is vital for the government and public health authorities in formulating relevant policies. The study contributes both practically and theoretically to the concerned field of pandemic management. Highlights• To examine the nexus among the COVID-19 confirmed cases, deaths, meteorological factors, and the air pollutant namely PM2.5 in six South Asian countries, from 1 March 2020 to 30 June 2020, employing advanced econometric techniques • Strong cross-sectional dependence and significant correlation among COVID-19 cases, deaths, meteorological factors, and the air pollutant • Temperature air pressure and humidity exhibit a significant impact on the COVID-19 confirmed cases in the six countries • COVID-19 confirmed cases and air pollutant PM2.5 have a statistically significant impact on the COVID-19 death cases • Results assist government and public health authorities in formulating the policies for containing, mitigating, and surveillance of COVID-19 in different countries

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“…Motivated by the increase of morbidity during autumn and winter, this study employed panel COVID-19 incidence data from Germany and scrutinized their relationship with weather data. In some studies, weather components like temperature were collected in categories such as average, maximum, and minimum level 52 , 56 – 58 , while others used daily average extracted on a defined regular interval 50 , 59 . Furthermore, in some other studies, either absolute humidity 59 , 60 or specific humidity 56 was employed instead of relative humidity.…”

Section: Introductionmentioning

confidence: 99%

Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany

Ganegoda

Wijaya

Amadi

et al. 2021

Sci Rep

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COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran’s $${\mathscr {I}}$$ I and global Geary’s $${\mathscr {C}}$$ C were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran’s scatter plot, where states’ dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.

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Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

Cited by 31 publications

References 33 publications

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia

Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany

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