Impact of climatic variations on the global distribution and prevalence of Mpox disease: Analysis of data from 52 countries using linear regression and time forecasting analyses

Impact of climatic variations on the global distribution and prevalence of Mpox disease: Analysis of data from 52 countries using linear regression and time forecasting analyses | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Impact of climatic variations on the global distribution and prevalence of Mpox disease: Analysis of data from 52 countries using linear regression and time forecasting analyses View ORCID Profile Iyanuloluwa S. Ojo , View ORCID Profile Olurotimi John Badero , Muhammad Aziz Rahman , Nicholas Aderinto , Onur Oral , Rishad Choudhury Robin , View ORCID Profile Emmanuel Jesuyon Dansu , Temitope Ogunjimi , Faridat Ibidun doi: https://doi.org/10.1101/2025.02.04.25321656 Iyanuloluwa S. Ojo 1 General Physician, University College Hospital Ibadan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Iyanuloluwa S. Ojo For correspondence: Iyanuloluwaojo7{at}gmail.com Olurotimi John Badero 2 Department of CardioNephrology Cardiac Renal & Vascular Associates Jackson Mississippi USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Olurotimi John Badero For correspondence: Timibade{at}yahoo.com Muhammad Aziz Rahman 3 Professor, Discipline of Public Health, Institute of Health and Wellbeing, Federation University Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Ma.rahman{at}federation.edu.au Nicholas Aderinto 4 Ladoke Akintola University of Technology Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Nicholasoluwaseyi6{at}gmail.com Onur Oral 5 Associate Professor, Faculty of Health and Sports Sciences, Ege University Turkey Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dronuroral{at}gmail.com Rishad Choudhury Robin 6 DrPH National Lead – Research The Leprosy Mission International , Bangladesh Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rishadcrobin{at}gmail.com Emmanuel Jesuyon Dansu 7 Graduate School of Life Sciences, Tohoku University , Sendai, Japan Department of Mathematical Sciences, Federal University of Technology , Akure, Nigeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emmanuel Jesuyon Dansu For correspondence: ejdansu{at}tohoku.ac.jp Temitope Ogunjimi 8 General Physician, University College Hospital Ibadan Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Justinjimmy77{at}gmail.com Faridat Ibidun 9 Registered Nurse, Nobles Hospital , The Isle of Man Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ibidunfaridat479{at}gmail.com Abstract Full Text Info/History Metrics Preview PDF Abstract MPOX infection has become a global health concern. Despite this global concern, the disease has shown epidemiological variations across different countries. This study examines the impact of climatic variations on MPOX prevalence and mortality using quantitative, cross-sectional approach. We analyzed daily confirmed MPOX cases from 52 countries using linear regression and Prophet models, focusing on average mean surface air temperature, precipitation, and climate types based on the Köppen classification. Globally, no significant relationship was found between temperature, precipitation, and MPOX prevalence (p-values 0.99 and 0.82); no significance was also noted for mortality globally (p-values 0.45 and 0.54). However, temperature had a more substantial impact on MPOX prevalence in Europe and South America (p-values of 0.049 and 0.0036, respectively). Precipitation was also significant in South America (p-value 0.044). The Prophet model, accounting for seasonality, revealed fluctuating influences of temperature and precipitation on MPOX prevalence, with their effects diminishing over time. Tropical and temperate climate types were most strongly associated with increased MPOX transmission. Monitoring these climatic factors, particularly in affected regions, is crucial for understanding and mitigating MPOX spread and its associated mortality. 1. INTRODUCTION Mpox was declared a public health emergency of international concern by WHO Director-General Dr. Tedros Adhanom Ghebreyesus in August 2024 [ 1 ]. This outbreak is part of recurring cycles, with the first multi-country outbreak starting in 2022 and being declared an emergency [ 1 ], [ 2 ]. Initially isolated from captive monkeys in 1959, the virus was first identified in humans in the Democratic Republic of Congo (DRC), which is the epicenter of the 2024 outbreak [ 3 ], [ 4 ]. As of May 1, 2024, Mpox has caused 225 deaths out of 103,429 cases globally [ 5 ], though this figure may be an underestimation, with reports suggesting over 1,000 deaths in the DRC alone [ 6 ], [ 7 ]. In non-endemic regions, where the virus is new, the population lacks pre-existing immunity, which can lead to more widespread transmission and potentially more severe outcomes [ 8 ]. While monkeypox is often self-limiting in healthy individuals, immunocompromised patients may experience more severe or prolonged illness [ 9 ]. There are two major strains: Clade 1, responsible for the 2024 outbreak in Africa, and Clade 2, which caused the 2022 outbreak in Europe [ 10 ], [ 11 ]. Mpox primarily spreads through close contact with infected animals or humans, especially through sexual contact [ 12 ], [ 13 ], [ 15 ], [ 16 ]. Symptoms include a rash with blisters or ulcers, fever, backaches, joint pain, exhaustion, and swollen lymph nodes, while some patients have been noted to have anal ulcerations [ 14 ], [ 15 ], [ 16 ], [ 17 ]. Viral infections and contact diseases have been shown to be influenced by climatic variations. Seasonal oscillations in a pathogen’s effective reproductive number have been observed for some viral infections, possibly driven by wintertime survival or relative wintertime immunosuppression [ 18 ]. For example, the influenza virus is more active in low daily temperatures, with a high incidence during cooler temperatures in temperate regions [ 19 ], [ 20 ]. Similarly, COVID-19, caused by the SARS-CoV-2 virus, was found to be modestly curbed by high temperatures [ 21 ]. Other studies suggested that COVID-19 transmission could be higher at an absolute humidity of 5-10g/m³ [ 22 ], [ 23 ]. These findings suggest that climatic factors might influence the increasing prevalence of certain viral contact diseases. The diverse and ambiguous distribution of MPOX raises questions about the factors influencing this pattern. For instance, the differing prevalence of MPOX between Kenya and Trinidad and Tobago is noteworthy. Kenya recorded 12 confirmed cases of MPOX, compared to Trinidad and Tobago’s 106 confirmed cases in 2024, despite Kenya’s proximity to the outbreak’s epicentre [ 7 ]. Furthermore, Trinidad and Tobago has a higher GDP per capita and adult literacy rate than Kenya, making the prevalence findings puzzling [ 24 ], [ 25 ]. Another curious observation is that no MPOX cases have been reported in countries like Morocco, Egypt, and Libya, while countries like Spain, Brazil, and China have seen a sharp rise in confirmed cases, despite the former being geographically closer to the outbreak’s epicentre [ 25 ]. There have been several instances of neighbouring countries with significantly different confirmed cases of MPOX. For example, Spain has recorded over 300 confirmed cases of MPOX while Morocco has none. Moreover, despite the Democratic Republic of Congo being the source of the outbreak, its eastern neighbour Rwanda has only recorded a few MPOX cases. While other factors might be involved in this differing prevalence between these countries, the fact that these countries have relatively different climate types suggests that climatic variations could play a role in explaining these differences and thus warrants further study. The potential influence of climatic factors on the prevalence and transmission of MPOX disease has been understudied. The combination of climate, environmental conditions, and human interactions with their surroundings can create conditions that increase the spread and impact of MPOX [ 26 ]. Since the 2022 outbreak, only one study has examined the impact of meteorological factors on MPOX prevalence, covering data from May 6, 2020, to October 10, 2022 [ 27 ]. There is a need to further investigate the influence of climatic variations on MPOX prevalence, particularly in the context of the 2024 outbreak, given the disease’s diverse and ambiguous global distribution. The aim of the study is to determine the influence of climatic variations, particularly the average mean surface air temperature and precipitation, on the prevalence of MPOX and the mortality associated with it. 2. MATERIALS AND METHODS 2.1 Daily MPOX Confirmed Cases and Mortality in 2024 We extracted 2024 MPOX confirmed cases and mortality data primarily from Our World in Data , the regional WHO bulletin for Africa, and the Joint ECDC-WHO Regional Office for Europe report [ 5 ], [ 28 ], [ 29 ]. This data includes the daily recorded number of patients confirmed with MPOX via a polymerase chain reaction (PCR) test and the number of deaths from MPOX per day. Additionally, we obtained data from several reputable news agencies across diverse countries when information was not available from the primary sources. The data were collected from January 1, 2024, to August 23, 2024. 2.2 Climatic Factors We utilized two important climatic factors: the average mean surface air temperature and precipitation in the form of rain, sleet, and snow. Data for each assessed country was extracted from the World Bank Climate Change Knowledge Portal [ 30 ]. This data was based on the climate context for the current climatology, 1991-2020, derived from observed historical data. We also used the Köppen Climate Classification types, derived from the National Geographic Society [ 31 ]. 2.3 Other Factors Assessed In order to limit omitted variable bias, we decided to also assess the impact of some factors outside the scope of our study. We derived the Adult Literacy Data from the UNESCO Institute for Statistics via the World Bank (2023) and Our World in Data [ 25 ], [ 32 ]. UNESCO defines literacy as the ability to read and write basic everyday statements. However, literacy standards differ across countries, and data from North America and Western Europe, which rely on more thorough assessments, cannot be easily compared with global data and could not be obtained. Net Migration Rate data was derived from the CIA’s The World Factbook , and the Gross Domestic Product (GDP) per capita was obtained from the International Monetary Fund [ 24 ], [ 33 ]. 2.4 Linear Regression Model A linear regression model was used in R to determine whether there is a linear relationship between the climatic factors extracted for each country and the prevalence and mortality of MPOX, both globally and continentally. The R package ‘ggplot2’ and the function ‘lm’ were employed to analyze the data using the linear regression model defined as: Where y represents MPOX prevalence or mortality, x 1 and x 2 …x p are the predictors (AMSAT and precipitation), β 0 is the intercept of the regression line, β1, β 2 …β P represent the strength and direction of the effect of each predictor on y, while ɛ accounts for variability not captured by the predictors. 2.5 Time Forecasting Model The automatic forecasting time series model, Prophet, was used in R to analyze the impact of climatic variations on MPOX prevalence globally. Developed by Meta, Prophet is primarily designed for time forecasting but can also be adapted to assess the influence of one variable on another. We chose Prophet for its robustness in handling time series data with seasonality and irregular patterns. Average mean surface air temperature and precipitation were added as external regressors to examine their impact on MPOX prevalence. The Prophet model was trained using each external regressor, generating forecasts for MPOX prevalence while accounting for seasonality. The R package ‘future’ was utilized to parallelize computations and ensure optimal efficiency during model training, while ‘ggplot2’ was employed to visualize the results. This included the forecast plot, trend and seasonal plots, and the regressor plot, which is of primary concern in our study. The analysis is expressed as: Where y(t) is the predicted value at time t, g(t) is the trend function, s(t) is the seasonal function, h(t) is the effect of other events, β is the coefficient showing the impact of the regressor, and x(t) is the value of the regressor at time t. 2.6 Inclusion Criteria For both the linear regression and time forecasting analyses, it is essential that a reliable dataset is available for the countries included in the study. For the time-forecasting analysis, selected countries must have reported at least 10 confirmed MPOX cases and must not be neighbouring the Democratic Republic of Congo, the epicentre of the 2024 outbreak and the most affected country. These inclusion criteria were established to minimize selection bias as effectively as possible. Download figure Open in new tab PRISMA-STYLED DIAGRAM SHOWING THE SELECTION PROCESS BASED ON THE INCLUSION CRITERIA. 2.7 Statistical Analysis We collated data using WPS Office Excel version 2021 and analyzed it using R software version 4.4.1. A linear regression model was employed to determine the relationship between climatic factors and MPOX prevalence and mortality in 2024. Additionally, the model assessed the impact of other factors such as the adult literacy rate, GDP per capita, and net migration rate. The evolution of MPOX infection was examined using the Automatic Forecasting Time-Series Model, Prophet, which accounts for seasonality—an aspect not considered by the linear regression model. In all models, climatic factors served as the dependent variables, while MPOX prevalence was the independent variable. The number of deaths from MPOX was also considered globally as an independent variable in the linear regression model. The relationship between these variables was verified using coefficients, the 95% confidence interval, the F-statistic, and the corresponding p-value. 3. RESULTS Table 3.1 shows the descriptive statistics of both the independent and dependent variables. The average mean surface air temperature globally was 18.03-degrees Celsius with a standard deviation of 8.25, a minimum of −4.03 which was recorded for Canada and a maximum of 28.17 which was recorded for the United Arab Emirates. The mean precipitation was 1049.06 with a standard deviation of 734.21, a minimum of 19.62 recorded for Egypt, and a maximum of 2996.28 recorded for Malaysia. An average of 140.42 cases were confirmed to be MPOX via the polymerase chain reaction (PCR) with a standard deviation of 495.74. The lowest number of cases confirmed was zero, which was recorded for multiple countries, while the highest number was 3235 for the Democratic Republic of the Congo, the source of the 2024 outbreak. The average number of mortality cases was 10.039 with a standard deviation of 70.84; the minimum number of deaths was zero, which was recorded for 88.5% of the countries studied, while the maximum was 511, which was recorded for the Democratic Republic of the Congo. View this table: View inline View popup Download powerpoint Table 3.1: Descriptive statistics for the climatic factors, MPOX prevalence and mortality from MPOX. The linear regression model analysis in Table 3.2 shows that the average mean surface air temperature and precipitation have no significant effect on the MPOX prevalence globally, with a 95% confidence interval of −17.17 to 16.98 and −0.17 to 0.21 respectively, a low adjusted R-squared of −0.020 and −0.019, respectively, and a p-value >0.05 (0.99 and 0.82, respectively). The global findings are echoed in Africa with a 95% confidence interval of −11.45 to 5.59 and −0.035 to 0.055, respectively, a low adjusted R-squared of −0.037 and −0.069, respectively, and a p-value of 0.47 and 0.64, respectively. The global findings are also echoed in Asia, with a 95% confidence interval of −17.15 to 0.81 and −0.079 to 0.052, respectively, a low adjusted R-squared of 0.18 and −0.069, respectively, and a p-value of 0.071 and 0.66, respectively. However, the average mean surface air temperature has a weak impact on MPOX prevalence when compared to the global findings. Europe findings are slightly opposite to the global findings. The AMSAT has a positive correlation with the MPOX prevalence in Europe with a 95% confidence interval of 0.10 to 31.45, a positive adjusted R squared value, and a p-value 0.05 (0.54). North America also echoed the global findings with a 95% confidence interval of −107.55 to 70.15 and −2.19 to 1.065, respectively, a low adjusted R-squared of −0.16 and 0.050, respectively, and a p-value of 0.55 and 0.35, respectively. In South America , findings were of significant opposite to the global findings. Both AMSAT and precipitation have a significant positive correlation with MPOX prevalence with a 95% confidence interval of 4.43 to 9.90 and 2.765e-03 to 0.11, respectively, a positive adjusted R-squared of 0.95 and 0.72, respectively, and a p-value of 0.0036 and 0.044, respectively. Only two countries, Australia and New Zealand, met the inclusion criteria in Oceania, which is not enough data for a linear regression model. View this table: View inline View popup Table 3.2: Climatic Variations associated with MPOX Prevalence using the Linear Regression Model. View this table: View inline View popup Download powerpoint Table 3.3: Climatic Variations associated with Mortality from MPOX using the Linear Regression Model. View this table: View inline View popup Download powerpoint Table 3.4: The Effect of Other Selected Factors on MPOX Prevalence. The linear regression model analysis in Table 3 shows that there is no significant correlation between the climatic variations assessed and the mortality from MPOX with a 95% confidence interval of −1.50 to 3.35 and −0.019 to 0.036, negative adjusted R-squared value of −0.008227 and −0.012 and p-value of 0.4484 and 0.54 for average mean surface air temperature and precipitation, respectively. The three other factors assessed do not have a significant influence on MPOX prevalence with a 95% confidence interval of −37.93 to 58.42, −12.71 to 13.14 and −4.58 to 3.97, negative adjusted R-squared values of −0.016, −0.020 and −0.020 and p-values of >0.05 (0.67, 0.95, 0.89) for migration, adult literacy rate and GDP per capita respectively. Based on the p-values, none of the three other factors assessed had a significant relationship with MPOX prevalence. From L-R: CD - Continental KC D, TA - Tropical KC A, DB - Dry KC B, ED - Extremely Diverse, HH - Highland KC H, MCD - Majorly Continental KC D, MTC - Majorly Temperate KC C, MTA - Majorly Tropical KC A, PE - Polar KC E, TC-Temperate KC C Figure 3.1 shows the Linear Regression analysis comparing the impact of climate type on MPOX prevalence globally. Tropical climate type (Koppen Classification Type A) had the highest impact on MPOX prevalence, with temperate climate type (Koppen Classification Type B) and extremely diverse climate coming close. Continental climate type (Koppen Classification Type D) and Polar climate (Koppen Classification Type E) have the lowest impact. However, the p-value is 0.98, which means that climate type generally has little significance in determining MPOX prevalence globally. Download figure Open in new tab FIGURE 3.1: THE GLOBAL EFFECT OF CLIMATE TYPE ON MPOX PREVALENCE IN THE LINEAR REGRESSION MODEL. Figure 3.2 shows the effect of the average mean surface air temperature on MPOX prevalence in the prophet model. The historical data up to September 2024 shows a tall, recurring pattern up to the positive threshold, which indicates that AMSAT has a strong positive correlation with MPOX prevalence at certain intervals during the observed months (January-August 2024). However, the horizontal line post historical data after October 2024 signifies a diminishing effect of AMSAT on MPOX prevalence over time. Download figure Open in new tab FIGURE 3.2: THE GLOBAL EFFECT OF THE AVERAGE MEAN SURFACE AIR TEMPERATURE ON MPOX PREVALENCE IN THE PROPHET MODEL. Figure 3.3 shows the effect of precipitation on MPOX prevalence in the prophet model. The tall, recurring spikes span across both positive and negative fields, indicating that precipitation has a strong influence on MPOX prevalence, alternating between increasing and decreasing MPOX prevalence at certain intervals from January to September 2024. However, the horizontal line post historical data from October 2024 shows a weakening influence on MPOX prevalence over time. Given more credence to this is that the horizontal line is at the negative zone. Download figure Open in new tab FIGURE 3.3: THE EFFECT OF PRECIPITATION ON MPOX PREVALENCE IN THE PROPHET MODEL. 4. DISCUSSION To the best of our knowledge, this is the second study to assess the impact of climatic variations on the disruptive MPOX outbreaks and the first to do so in the context of the recent 2024 outbreak. Both the average mean surface air temperature (AMSAT) and precipitation had no significant relationship with MPOX prevalence and mortality globally based on the linear regression model. However, AMSAT was influential on MPOX prevalence in South America and Europe, while precipitation was significant on MPOX prevalence in South America. This could be explained by the climatic diversity on each continent. When compared to the other continents, excluding Antartica and Oceania, South America and Europe are the least diverse in climatic types. Most of South America is of the tropical and subtropical climates, which have been found to be the most favorable climate type for MPOX prevalence in this study, with the Amazon basin encompassing most of the continent [ 34 ]. Europe majorly has the temperate climate type and the continental climate types in a few countries [ 34 ]. The temperate climate type is also favorable to MPOX prevalence based on this study. Precipitation in South America is very high, especially around the Amazon Basin. This high precipitation is a conducive environment for known vectors of the MPOX disease, such as monkeys and squirrels and can ecologically influence the MPOX disease prevalence [ 35 ]. Since climate types are extremely diverse in Africa, Asia, and North America, the varying temperature and precipitation might mask the influence of the average mean surface air temperature and precipitation on MPOX prevalence in these regions. The Average Mean Surface Air Temperature (AMSAT) shows a positive correlation with MPOX disease in countries already affected, as indicated by the Prophet model. However, its impact on MPOX prevalence is globally weak, with a p-value of 0.99 in the linear regression model. The flat line after the historical data in the Prophet model suggests a weak relationship after September. This finding is consistent with a study by Islam et al. (2022), which also found a positive correlation between temperature and daily monkeypox cases [ 27 ]. This indicates that AMSAT might play a significant role in affecting the virulence and severity of MPOX, but it does not appear to have a notable impact on the overall incidence or occurrence of the disease. Temperature has been shown to influence the virulence of the pox virus in experiments with chick embryos [ 36 ]. However, the impact of temperature on virus virulence can vary; for example, a study on COVID-19 found that both extremely high and low temperatures significantly affected SARS-CoV-2 virulence [ 21 ]. In contrast, our study found that extreme temperatures tend to reduce MPOX prevalence, as shown in the tables. Thus, a positive correlation with temperature appears to hold true only within a specific range. Precipitation exerts a dynamic influence in countries already affected by the MPOX virus, according to the Prophet model. However, its global influence on MPOX is weak, with a p-value of 0.82 in the linear regression model. The horizontal flat line post-historical data suggests that the impact of precipitation on MPOX prevalence might depend on other factors not accounted for in the forecasted period. This suggests that precipitation does not influence MPOX prevalence globally, which contrasts with a study showing increased diarrhoea prevalence in sub-Saharan children with low rainfall during the dry season [ 37 ], [ 38 ]. Precipitation has also been reported as a negative driver of cough, diarrhoea, and fever in another study, indicating a potential dynamic two-legged influence of precipitation, as observed in this study [ 39 ]. High precipitation beyond 1500 mm has, however, been associated with a high prevalence of MPOX in South America. The strong effect of AMSAT on MPOX prevalence in tropical and temperate climates could also be explained by the virus’s mode of transmission. MPOX is primarily transmitted through contact with animals such as pouched rats, rope and sun squirrels, African dormice, monkeys, pigs, hedgehogs, and rodents, and secondarily through human-to-human contact, with sexual contact being prevalent among men who have sex with men [ 40 ]. It can also be transmitted via contaminated surfaces or cloth. The significant impact of temperature on MPOX prevalence in already affected countries might be due to increased sympathetic activity, which activates cholinergic fibres supplying sweat glands, leading to increased sweating and a higher chance of contact with contaminated items [ 41 ]. MPOX potential for sustained human-to-human transmission through close contact and contaminated materials raises significant concerns and should not be underestimated [ 42 ]. High temperatures have also been reported to enlarge skin pores [ 43 ]. Conversely, precipitation does not affect MPOX transmission as AMSAT does. However, extremely high precipitation (rainfall) is conducive for vectors of the MPOX disease, particularly monkeys, while low precipitation can cause dehydration and dry skin, making it harder for the virus to penetrate the skin barrier. This explains why high precipitation in South America strongly influences MPOX prevalence. Our findings have significant implications. They elucidate the varying prevalence of MPOX across countries with similar net migration rates, economic influences, and literacy rates. For example, despite being close geographically, Spain and Morocco show different MPOX cases. Spain, with a temperate climate and a precipitation level of 623.07 mm, has recorded 352 MPOX cases in 2024. In contrast, Morocco, with a dry climate and a lower precipitation level of 315.87 mm, has not reported any MPOX cases in 2024. Libya, a neighbouring country to Morocco with similar climatic conditions, has also reported no MPOX cases. This study suggests that tropical and temperate climates are more conducive to MPOX relative to other climate types. RECOMMENDATIONS Based on the study findings, we recommend that the World Health Organization, the Centers for Disease Control and Prevention, and national health agencies emphasize the importance of staying indoors in countries where MPOX is already prevalent, particularly on sunny days. This advice should apply to countries with precipitation values above 1500 mm and those with tropical and temperate climate types. Staying indoors will help reduce the spread of MPOX in conditions where contact is more likely and protect vulnerable populations, especially children. Practical advice on maintaining good physical and mental health while indoors should also be provided. Additionally, tropical and temperate countries affected by MPOX should regularly monitor temperature changes, particularly in regions prone to temperature fluctuations. Implementing early warning strategies based on temperature trends in areas susceptible to MPOX would be beneficial. Countries with precipitation values above 1500 mm should also enforce stricter health preventive measures and enhance health awareness to prevent the incidence or spread of MPOX. There is a need to conduct localized research on the impact of climatic variations on MPOX prevalence at national and subcontinental levels, particularly in continents with extremely diverse climates. We also emphasize the importance of a ring vaccination approach to curb the spread of MPOX, as recommended in a previous study [ 44 ]. Our findings highlight that those susceptible to the disease are often clustered within similar climatic conditions. LIMITATIONS Linear regression assumes a linear relationship between the independent variables (AMSAT and precipitation) and the dependent variables (MPOX prevalence and mortality). This assumption may oversimplify the relationship if non-linear effects are present. However, the Prophet model accounts for seasonality and offers a robust alternative to manage this limitation. There is also a potential for omitted variable bias, as the study focused solely on temperature and precipitation as predicting factors. Other variables may significantly influence MPOX prevalence. To address this bias, the study assessed the impact of adult literacy rate, net migration rate, and GDP per capita. Nonetheless, factors such as population density were not included in the analysis. The findings may not be universally applicable to regions with highly diverse climates, such as the United States, Canada, Russia, and Argentina. In-depth, region-specific studies are needed in these countries to understand how climatic variations affect MPOX prevalence locally. CONCLUSION The findings suggest that climate plays a significant role in MPOX dynamics, particularly in regions with less climatic diversity like South America and Europe. High temperatures, along with precipitation levels near and above 1500 mm, are particularly significant in the transmission of the MPOX virus. Additionally, tropical and temperate climates have been observed to be conducive to the spread of MPOX disease. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding No funding was received for this study. Author contributions statement ISO made substantial contributions to the conceptualization of the study, acquisition, analysis, and interpretation of data, drafting of the first and final manuscript, and supervision of other authors; OJB made substantial contributions to the interpretation of data, drafting of the first and final manuscript. MAR made substantial contributions to the interpretation of data and drafting of the first and final manuscript. NA made substantial contributions to the interpretation of data and drafting of the first and final manuscript. OO made substantial contributions to the interpretation of data and drafting of the first and final manuscript. RCR made substantial contributions to the interpretation of data and drafting of the first and final manuscript. EJD made substantial contributions to the interpretation of data and drafting of the first and final manuscript. TO made substantial contributions to the interpretation of data and drafting of the first and final manuscript. IF made substantial contributions to the acquisition and analysis of data and drafting of the first and final manuscript. All authors read and approved the final manuscript. All authors agreed to be accountable for all aspects of the work. Data Availability Statement The data that support the findings of this study are available from the corresponding author, ISO, upon reasonable request. Acknowledgement No acknowledgement for this study. References 1. ↵ WHO Director-General declares mpox outbreak a public health emergency of international concern . ( 2024 ). Accessed: Aug. 26 : https://www.who.int/news/item/14-08-2024-who-director-general-declares-mpox-outbreak-a-public-health-emergency-of-international-concern 2. ↵ “What you need to know about monkeypox,” World Economic Forum . ( 2024 ). 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