Influences of weather-related parameters on the spread of Covid-19 pandemic - The scenario of Bangladesh.

OBJECTIVE: Weather parameters such as temperature, humidity, air quality index and wind speed are the important factors influencing the infectious diseases like Covid-19. Therefore, this study aims to discuss and analyse the relation between weather parameters and the spread of Coronavirus disease (Covid-19) from the perspective of Bangladesh.
METHODS: Correlation among weather parameters and infection and death rate were established using several graphical plots and wind rose diagrams, Kendall and Spearman correlation and appropriate discussion with relevancy and reference. Information presented in this study has been extracted from 1st April 2020 to 30th December 2020.
RESULTS: Analyses show that with the decrease in temperature, infection rate increased significantly. Also, the number of infection increases as wind speed increases. As the absolute humidity rate of Bangladesh is almost constant; therefore, the authors are unable to predict any relation of absolute humidity with the number of infection. Further, the prediction for the number of infections based on the wind direction for the several regions of seven divisions in Bangladesh is vulnerable for the upcoming several months.
CONCLUSION: This study has analysed the dependency of weather parameters on a number of infections along with predicting the upcoming danger zones.

Introduction

A carcinogenic etiological virus outbroke from Wuhan, China, on 31st December 2019 (Ghinai et al., 2020) has become the reason for the record-breaking deaths of more than 1.4 million (up to 2nd December 2020) so far in this decade (Worldometer, 2020). This unspecified virus was recognised firstly as Covid-19, which was later termed as Severe Acute Raspatory Syndrome Coronavirus 2 (SARS-COV-2) in February 2020 (Islam et al., 2020a). This deadly infectious virus had spread its contagions in Americas (Confirmed cases-26,875,671), Europe (Confirmed cases-19,053,245), South-east Asia (Confirmed Cases-10,878,115), Eastern Mediterranean (Confirmed Cases-4,147,916) Africa (Confirmed cases-1,512,542) and Western Pacific (Confirmed cases-892,004) till 2nd December 2020 (WHO, 2020a). Generally, this virus with a size of 1–2 nm is associated with an enveloped group of viruses including a single strained RNA with a positive sense (Fehr and Stanley, 2015; Ebrahimi and Rahim, 2020). People get infected through this virus either from the direct contact of the respiratory droplets from the infected person or through the infected areas. Though the virus can survive over a surface for more than an hour, the surface can be disinfected with the help of any disinfectants (Lai et al., 2020). The symptoms and signs of these contagious diseases include fever, breathing problem, cough; the condition gets more serious often when it leads to a severe respiratory problem as well as pneumonia (Huang et al., 2020). The infection can be turned into a fatal condition in some rare cases, also (Huang et al., 2020).

The pandemic is still a matter of havoc for all mankind at this stage. Besides the direct contacts of the infected people or surfaces, researchers also found a nexus between this novel coronavirus with climate conditions (Wacker and Michael, 2013; Casanova et al., 2010). Researches from epidemiological studies found that ambient temperature can play a significant role in transmitting and surveillance of coronaviruses like SARS-COV-1 and the Middle East respiratory syndrome coronavirus (MERS-COV) (Casanova et al., 2010; Lu et al., 2020a). Meteorological parameters like temperature (T), absolute humidity (AH) areconsiderable general factors for spreading of contagious diseases (Lu et al., 2020a; Chan et al., 2011). Researchers also reported that alongside the transmission between human-to-human, meteorological variables are also the crucial driver in escalating the variability of viruses (Li et al., 2020). For instance, Bloom-Feshbach et al. (Bloom-Feshbach et al., 2013) suggested that the possibility of spreading out the Covid-19 is more in countries with cold temperature than the tropical and warm countries. Like SARS-COV-1, the surveillance in the different surface of SARS-COV-2, which is also referred to as Covid-19 depends on some specific weather condition (Van Doremalen et al., 2020). An observation on weather conditions of different countries in the world investigated from November 2019 to February 2020 reported that countries lying in similar latitudes (20–50° North) tend to be affected the most (Sajadi et al., 2020). The temperature and relative humidity of these countries are almost similar and range from 5 to 11 °C and 47–79% respectively. Another analysis using linear regression done by Oliveiros et al. (Oliveiros et al., 2020) demonstrated the similarities between weather parameters and increasing rate of cases. The study was done in China in the period of 23rd January to 1st March 2020 and found that the doubling rate of affected cases correlates with temperature positively and negatively in humidity. Though the correlation with relative humidity was very low for a strong conclusion, the study rigorously observed the relation of temperature, precipitation and wind speed. The recent study of Bukhari and Jameel (Bukhari, 2020) on emerging new cases between 20th January to 19th March 2020 found the absolute humidity plays an important role for spreading than the temperature and relative humidity. The study also found that 90% of the new cases until 21 March 2020 had occurred within a specific range of AH (4 to9 g/m3) and T (3 to 17 °C). While this information on the global trend is valuable, to date, rare studies are available dealing with relationships between weather and Covid-19 spread have been established for any specific country. However, Bangladesh consists of a complex set of meteorological properties and highly dense people make it more vulnerable. Up to 2nd December 2020, 4,69,423 cases and 6713 deaths have been confirmed in 62 out of 64 districts (Wacker and Michael, 2013). Though during the earlier outbreak of Covid-19 in Bangladesh, it experienced lockdown to mitigate the effect, the inadequacy of testing kits, unawareness and negligence for home quarantine and isolation have inflated the condition (Bodrud-Doza et al., 2020). Because of densely high, illiteracy, economic insolvency and inadequate research and medical facilities result in growing positive confirmed cases and mortality rate rapidly. Besides these, the correlation between meteorological conditions with confirmed cases has been rigorously studied yet. Though Islam et al. (Islam et al., 2020b) did a significant contribution in studying the interrelation between them but their studies were done within the summertime and didn't conclude with the prediction for spreads.

Though many studies regarding the spread of Covid-19 have been conducted so far, comprehensive discussion and analysis from the perspective of Bangladesh are still limited. Further, no research could predict the upcoming outbreak situation (spread of the virus in the upcoming months) in Bangladesh. Therefore, keeping these in the mind and the limited literature in scope, authors have discussed the spread of Covid-19 in Bangladesh based on meteorological conditions (temperature, relative and absolute humidity, air quality index and wind speed). This research also tried to predict the upcoming virus spread based on wind data (wind rose diagram). The prediction shows the possible regions of Bangladesh where there is a possibility that the number of infected people may rise and necessary precautions should be taken. Also, this is the first detailed study which discusses the spread of Covid-19 based on wind speed in Bangladesh and also draws possible locations of virus spread in the upcoming 3–4 months.

Materials and methods

The study has been conducted for the seven major divisions of Bangladesh, including Dhaka, Rajshahi, Chittagong, Sylhet, Barisal, Khulna and Rangpur. Barisal division is located in the south-central part of Bangladesh. Barisal consisting a total area of is 13,644.85 km2 approximately and is surrounded by the Bay of Bengal on the south, Dhaka Division on the north, Khulna Division on the west and Chittagong Division on the east. Another northeastern division named Sylhet- is touched by some of the Indian states of Meghalaya, Assam and Tripura to the north side, south and east, respectively; and in the southeast, it is bounded by the Chittagong division of Bangladesh and Mymensingh and Dhaka to the west. Further, Rangpur division lies on the northernmost part of Bangladesh. Rangpur and Dinajpur are the two major cities of this division. Though there is another new division of Bangladesh named Mymensingh which was declared as a division in 2015. However, Mymensingh division is not considered in this study due to the unavailability of enough data (Divisions of Bangladesh, 2020).

Data of Covid-19 for Bangladesh were considered from 1st April 2020 to 30th December 2020. Data were collected from several trustworthy and verified sources such as the official website of Covid-19 Bangladesh (Covid-19, 2020), WHO (WHO, 2020b), Institute of Epidemiology Disease Control And Research (IEDCR) (IEDCR, 2020) Press Briefing, Management Information System (MIS) (MIS, 2020), Directorate General of Health Services (DGHS) (DGHS, 2020) and Anadolu Agency (Agency, A, 2020). Data of wind speed, temperature, humidity and air quality were obtained from Accuweather (Accuweather, 2020), Weather Atlas (Atlas, W, 2020), WeatherOnline UK (UK, W, 2020) World Bank (Bank, W, 2020) and Dhaka US Consulate (Consulate, D.U, 2021).

Fig. 1 shows the divisional map of Bangladesh with some information related to Covid-19. It can be seen from Fig. 1 that more than 50% of confirmed cases are found in Dhaka division. Also, the mortality rate is also high in the Dhaka division compared to other divisions followed by Chittagong and Khulna. Further, the mortality rate, according to sex distribution, male patients hold the lions share of almost 78%. Further, the higher mortality rate is found between the ages range from 61 to 70, which is almost 40%, followed by ages range from 51 to 60 and 41–50, which hold approximately 27% and 13% respectively.

Fig. 1

Divisional map of Bangladesh with Covid-19 information (data on request from IEDCR, MIS and DGHS). Arrows in the figure show wind direction (Yellow- January, Blue-April, Green-July, Black-October). (The map outline structure is taken from (VectorStock.com/1608043, n.d.)). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

In this study, the effect of meteorological parameters on spreading the Covid-19 has been analysed, and its correlation with the infected number has also been found by statistical analysis. The collected data have been presented as the possible interpretation of the relationship between meteorological properties (temperature, relative humidity, absolute humidity and air quality index) with the number of infected people. The analysed and predicted results are correlated with the previously published literature data on Covid-19 and influenza viruses. For analysing the wind speed and its direction, the wind rose diagrams have been depicted for seven divisions of Bangladesh. It requires several data such as wind direction, wind speed and location coordinates (X, Y) of meteorological stations to yield the wind profile outputs. From the wind rose diagram, a prediction has been made on the spread of Covid-19 in those divisions for an upcoming three-four months. Further, from the data of relative humidity and temperature, absolute humidity is calculated with the help of Eq. (1), which is also considered as a fundamental parameter to analyse the current spread of deadly Covid-19.

Here, T is the dry bulb temperature of the air, which in the degree Celcius (°C) unit.

Climate dependency was also evaluated using Kendall's Tau correlation test to establish a relation between temperature and infected rate, and wind speed and infected rate using Eq. (2). This correlation is used to find out the ordinal co-relation between the measured quantities. It is a non-parametric hypothesis test for statistical analysis and does not rely on any assumptions (Singh and Agarwal, 2020).where τ is Kendall's coefficient and n is the number of cases.

Besides, the Spearman rank correlation method is also used to determine the climate dependency. A relation between wind speed and infected rate, and temperature and the infected rate is determined using this relationship. Regardless the data is linear or not correlation assesses monotonic relationships between variables. When each of the variables is a seamless monotone function of the other, a correlation of +1 or −1 occurs if there is no repetition in the data. The reason behind using Spearman rank correlation is that most of the correlation methods works better when the datasets are linear, but Spearman rank correlation works fine for both linear and monotonic datasets and also it works with the ranks of the datasets and identifies nearly accurate relationship. Since our dataset does not move in a linear pattern, therefore, Spearman rank correlation is more accurate here. Eq. (3) shows the Spearman rank correlation equation. Table 1 shows the data for calculating Kendall and Spearman rank correlation coefficient (Mofijur et al., 2020).

Table 1

Values for calculating Kendall and Spearman rank correlation coefficient.

Month	Confirmed cases	Temperature (°C)	Average wind speed
March	51	27	8.8
April	7616	28	12.8
May	39,486	29	15.4
June	98,330	30	17.6
July	92,178	30	17.2
August	54,964	30	17.6
September	49,000	30	14.5
October	43,000	29	8.5
November	56,000	29	7.40
December	46,000	26	7.72

Results and discussion

Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10 show the effect of temperature (dry-bulb temperature, °C), relative and absolute humidity and particulate matter (PM) on the infected numbers/transmission of the virus. The statistics have been taken from April to December of 2020. A close observation from the graphs shows that for the months of April–October, the temperature reduces gradually and very slightly though the number of cases increased rapidly. The Kendall correlation coefficient shows a value of +0.572 with the temperature and the number of infections, whereas, Spearman rank correlation coefficient shows a value of +0.7712, which shows the high impact of temperature on increasing the infection rate. It is necessary to mention that along with temperature, the humidity and wind speed also play a vital role in influencing the number of cases. The analysis shows that the number of infected people has a dependency on temperature. However, different works of the literature suggest some information which contradicts our findings. A study (Xie and Zhu, 2020) mentioned that it could not find any declination in the number of infected people with the temperature rise. Shi et al., (Shi et al., 2020) suggested that the number of confirmed cases has a biphasic relationship with temperature and also predicted that with the increase of temperature, the infected cases reduces, which supports our findings for Bangladesh. Though the graphs may mislead some readers that with the increase of relative humidity, the infection rate increased exponentially. However, this is not the case in practice. In this research, higher humidity level tends to decrease the number of infected people and the transmission of the virus. However, it can be seen from the graphs that they are dominated by temperature and wind velocity. The number of infected people increases because of the low temperature and high wind velocity (Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11 ). Literature suggests that with the increase of relative humidity, the number of cases reduces (Ahmadi et al., 2020). It was reported earlier that at a low humidity level, respiratory diseases spread faster and results in higher infection rates (Davis et al., 2016). A study of more than 25 years' of data demonstrated that the low humidity is one of the main reasons for the high mortality rate by influenza-related mechanisms (Barreca, 2012). Reduction of mucociliary clearance and/or epithelial damage may be caused by the breathing of dry air, thereby making the host more vulnerable to respiratory virus infection. Exhaled respiratory viruses settle quickly at high humidity; therefore, it is hard to spread for them (Barreca, 2012).

Fig. 2

Interpretation of the relation between meteorological parameters and infected numbers on April 2020.

Fig. 3

Interpretation of the relation between meteorological parameters and infected numbers on May 2020.

Fig. 4

Interpretation of the relation between meteorological parameters and infected numbers in June 2020.

Fig. 5

Interpretation of the relation between meteorological parameters and infected numbers in July 2020.

Fig. 6

Interpretation of the relation between meteorological parameters and infected numbers in August 2020.

Fig. 7

Interpretation of the relation between meteorological parameters and infected numbers in September 2020.

Fig. 8

Interpretation of the relation between meteorological parameters and infected numbers in October 2020.

Fig. 9

Interpretation of the relation between meteorological parameters and infected numbers in November 2020.

Fig. 10

Interpretation of the relation between meteorological parameters and infected numbers in December 2020.

Fig. 11

Effect of wind speed on the spread/transmission of the virus.

In Fig. 11, the effect of wind speed on the transmission of the virus has been represented. It can be seen from Fig. 11 that with the increase of wind speed, the spread/transmission increases. As recent studies suggest that the virus can spread through the air (Lu et al., 2020b); therefore, more velocity of the wind can take more virus with it, which is visible from Fig. 11. The Kendall's Tau correlation coefficient shows a value of +0.73 with the average wind speed and infection rate, whereas, Spearman rank correlation coefficient shows a value of +0.943, which shows a very strong impact of wind speed on the increase of the number of infections. Along with wind speed, wind direction is also a fundamental factor, which has been demonstrated with the wind rose diagrams in the next section. However, it is also necessary to mention that the wind speed is related to the transmission, not with the survival or lifetime of the virus. Another vital reason for the high spread of the virus is the violent thunderstorms during the month of March to May, which produces a wind velocity of 60 km/h. During the early summer and late monsoon season when the intense storms occur and a wind speed of 160 km per hour can be observed.

Particulate matter such as PM10, PM2.5, PM1 and PM0.1 is usually defined as the fraction of particles with an aerodynamic diameter smaller than 10, 2.5, 1 and 0.1 μm (1 μm = 1 millionth of a meter or 1 thousandth of a millimetre) respectively. Regarding PM10 and absolute humidity, some researchers suggest that with the increase of absolute humidity, the number of infected people decreased (Ebrahimi et al., 2020; Liu et al., 2020). However, as the absolute humidity of Bangladesh is almost constant (no significant variation); therefore, the authors are unable to predict any relationship between absolute humidity and the number of infections. Further, the data suggests (Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10) that the PM10 rate tends to reduce from April to October, which hinders the spread of the virus, though the other factors (temperature, wind speed and direction) are working in favour of the virus. Data for Bangladesh indicates that at the beginning of 2020 (January, February and March) the PM2.5 rate was very high in the air. In January and February, the PM2.5 rate was around 250. However, in the following months (from April to October), the average PM2.5 rate was near 80 (sometimes less than this). But, from November 2020, the PM2.5 rate has started to increase again and gradually again, reaching the unhealthy state (Consulate, D.U, 2021). It is important to notice that the reduction of PM rate happened due to the lockdown in Bangladesh from March 2020 mainly. During the lockdown, all sorts of industries were kept shut down and the movement of the transportation was prohibited, resulting in lower PM in the air. As a result, it is evident that low temperature along with the high wind speed and its direction is the main reason for the transmission and spread of Covid-19.

In the highly populated cities such as Dhaka and Chittagong, the fast spread of Covid-19 is not only due to the wind speed and other weather parameters but also due to the high level of human mobility. Being the capital and industrial port of Bangladesh, these two places are considered the centre of almost all economic, commercial, and cultural activities of Bangladesh. Further, these two cities are highly populated, favouring the spread of the virus. According to census data, there are 21 million people currently living in Dhaka and has a density of 121,720 residences per square mile (Mofijur et al., 2020). Majority of the people live under the poverty line and almost 800,000 people are involved in garment industries or other unauthorised work. Whereas, Chittagong has a population of approximately 10 million. Such statistics make these two cities the epicentre for infection compared to other cities of the country. Coşkun et al. (Coşkun et al., 2021) also indicated that besides wind velocity population density is also a major factor for the spread of the virus. Studies report that it is extremely difficult to control the spread of the virus in windy weather. The intensity of wind and direction plays the key role here and Chittagong is the windiest region in Bangladesh. Therefore, going out in the wind without any medical protection is one of the main causes of the spread of the virus in Bangladesh.

Studies suggest that within a couple of seconds respiratory droplets of bigger size (between 400 and 900 μm) can travel at a moderate speed of up to 5 m in an urban setting. More importantly, smaller droplets can travel up to 11 m within less than 14 s (Meštrović, 2020). Statistics of Bangladesh shows that compared to women, men are more susceptible to Covid-19. The reason behind this is that among men almost 84.76% of male in Bangladesh works outside the home and among women on average almost 30% work outside. Higher wind speed is not only associated with the number of infected people but also with the mortality rate. From October, because of low wind speed, a decrease of 21.6% in weekly deaths can be observed. According to recent data, between the age of 61–70 the highest mortality rate can be observed (31.5%), whereas lowest in the age group of 31–40 (19.3%) (Fig. 12 ). Further, statistics suggest that comorbidities increase the chances of infection. Studies indicate that people with serious underlying medical conditions, especially those in long-term care facilities are having a greater risk of being infected by Covid-19. Elderly people having chronic health conditions such as lung disease diabetes and cardiovascular illness has the highest chance of death. People with underlying unrestrained medical conditions for instance lung, diabetes; hypertension; cancer patients on chemotherapy; liver, and kidney disease; patients taking steroids; smokers and transplant recipients are at augmented risk of COVID-19 infection (Sanyaolu et al., 2020). People with asthma may lead asthmatic attacks, acute respiratory distress and pneumonia if infected by Covid-19 (CDC, 2020).

Fig. 12

Age-sex distribution of COVID-19 case and death, 08 March −20 December 2020,

According to Khatun and Khatun (2020) because of human activities the air and surface temperature of an urban area tend to become higher than its surrounding rural areas. This phenomenon is often referred to the urban heat island (UHI) which is characterised by rising the sensible heat flux due to urbanisation and human activities. According to Santamouris et al. (2007); Akbari et al. (1998) and Oke (1982), releasing of anthropogenic heat at a high amount and air pollutants due to rapid industrialisation and transportation are considered as one of the major factors for generating UHI. It is worth to be mentioned that Bangladesh experiences a hot, humid summer from March to June. During June to October, a cool, rainy monsoon season appears and a cool, dry winter from October to March. Generally, during summer, maximum temperatures range between 30 °C and 40 °C. April is considered as the warmest month in most parts of the country (Cimates of the World/Bangladesh, 1999-2021). Further, COP24: UN Climate Change Conference in Poland indicated that the capital of Bangladesh (Dhaka) has already turned into an Urban Heat Island (UHI) and day by day the situation is becoming worse. The temperature of Dhaka is almost 2-3 °C higher than in rural areas (Chowdhury, 2018). Therefore, Dhaka is facing problems including heightened survival rate of novel germs and vectors. As a result, diseases such as chikungunya, dengue and respiratory diseases are increasing, which directly favouring the comorbidities. However, the variations of UHI during the pandemic and for the strict locked down can be summarised by analysing the temperature over the period of April to December 2020 (Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10). However, it can be seen that the average temperature of the month on May–June (Fig. 3, Fig. 4) are considerably lower than April (Fig. 2) and July to September (Fig. 5, Fig. 6, Fig. 7). This data indicates a positive impact of locked down on human activities which lead lower urban heat island intensity (UHII) which refers to the magnitude of an UHI or the degree of development of the UHI (Khatun and Khatun, 2020). It is a clear evidence of relaxation of human activities in terms of lowering the weather temperature even during in the hot and humid summer season. Bangladesh had faced a strict locked down from 26 March which had relaxed on 1st June 2020, though there were some restrcitions for some parts of country (Ketchell, 2020). During this time, human activities in terms of industrialisation and transportation have occurred in a lesser amount which lead to the lower weather temperature as well as lower UHII. After 1st june 2020, government lifted the lockdown and reopened offices, industries and markets except educational institutions. Though there was no lockdown, but government made some rules and regulations whuch were imposed on peoples daily activities such as grocerry stores and shops were instructed to shut down at 6 PM and any sorts of human gathering for any reason was strictly prohibited. But, these rules did not affect the increase of the temperature much.

The direction and speed of wind of the seven divisions of Bangladesh are shown in Fig. 13(a)–g. The wind rose diagrams have been depicted taking the wind speed and direction for the month from July to December 2020. Previous studies suggest that wind speed and the direction have a significant impact on the transmission of Covid-19 (Rendana, 2020; Bashir et al., 2020; Sarkodie and Owusu, 2020). The virus used to spread faster in the direction of wind (Rendana, 2020) as it can survive and remain active in the air for a couple of hours (Reuters, 2020). The wind rose diagram of Barisal shows that the wind direction was mainly in a north-northwest and north direction because of the monsoon. Covid-19 infected data also shows that the north-northwest regions of Barisal had the highest number of infected people in Barisal division in those four months. Similarly, the diagrams of Chittagong, Dhaka, Khulna, Rajshahi, Rangpur and Sylhet show the direction in north and east-northeast, north-northwest, north-northwest, north and north-northwest, west-southwest and north-northeast, east-northeast direction, respectively. As the virus can remain alive in the air, the wind carries the virus with it and it can then infect more people in that particular area (Rendana, 2020). Wind class frequency distribution reveals that the wind speed on average blows 10–25 km/h for all months, hence, it can be predicted that the prevailing virus velocity might be at this mentioned wind speed range. It is important that the highest wind speed also must be taken into consideration.

Fig. 13

The wind rose diagram of seven divisions of Bangladesh. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

It is very important to mention here that in seven divisions, according to the wind direction, the regions from northwest to east-northeast direction are in the danger zone for upcoming three-four months as the statistical data of Bangladesh shows that in the upcoming three-four months' wind will blow to these directions. Therefore, these regions should take extra cautions to hinder the spread of the virus. Some studies suggest that keeping a distance of six feet among ourselves can hinder the spread of the virus. However, most recent studies inform that six feet may not be enough in windy weather as at a wind speed of 4–15 km/h saliva droplets from sneezing and coughing can travel up to 18 ft (Muenz, 2020). Therefore, it a matter of concern for Bangladesh as the average wind speed in Bangladesh is more than 15 km/h. Moreover, in such a case, the highest wind speed should also be taken into consideration.

Conclusion

This is the first detailed study which evaluates the spread /transmission of Covid-19 in Bangladesh based on air quality index and wind speed. Along with these, the study also analyses the effect of temperature and humidity on the spread of coronavirus in Bangladesh. Results suggest that low temperature and high wind speed are the major weather conditions which are responsible for the rapid spread of the virus in Bangladesh. Also, in seven divisions, according to the wind direction, the regions from east-northeast to south direction are in the danger zone for the upcoming 3–4 months as the statistical data of Bangladesh shows that in the upcoming three-four months' wind will blow to these directions. Therefore, these regions should take extra cautions to hinder the spread of the virus. This prediction methodology is very important for other countries as well to predict the regions where the spread may increase in future months.

Declaration of Competing Interest

Authors declare no conflict of interest.