Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

Background

In the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate.

Methods

To mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021.

Results

The linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively.

Conclusion

The influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

A proposed One Health approach to control yellow fever outbreaks in Uganda

Yellow Fever (YF) is an acute viral hemorrhagic disease. Uganda is located within the Africa YF belt. Between 2019 and 2022, the Ugandan Health Authorities reported at least one outbreak of YF annually with an estimated 892 suspected cases, on average per year. The persistent recurrence of this disease raises significant concerns about the efficacy of current response strategies and prevention approaches. YF has been recognized as a One Health issue due to its interrelatedness with the animal and environmental domains. Monkeys have been recognized as the virus primary reservoir. The YF virus is transmitted through bites of infected Aedes or Haemagogus species mosquitoes between monkeys and humans. Human activities, monkey health, and environmental health issues (e.g., climate change and land use) impact YF incidence in Uganda. Additionally, disease control programs for other tropical diseases, such as mosquitoes control programs for malaria, impact YF incidence.This review adopts the One Health approach to highlight the limitations in the existing segmented YF control and prevention strategies in Uganda, including the limited health sector surveillance, the geographically localized outbreak response efforts, the lack of a comprehensive vaccination program, the limited collaboration and communication among relevant national and international agencies, and the inadequate vector control practices. Through a One Health approach, we propose establishing a YF elimination taskforce. This taskforce would oversee coordination of YF elimination initiatives, including implementing a comprehensive surveillance system, conducting mass YF vaccination campaigns, integrating mosquito management strategies, and enhancing risk communication. It is anticipated that adopting the One Health approach will reduce the risk of YF incidence and outbreaks.

Climate change and cutaneous leishmaniasis in the province of Ghardaïa in Algeria: A model-based approach to predict disease outbreaks

BACKGROUND

Cutaneous leishmaniasis (CL) is a vector-borne disease prevalent in Algeria since 2000. The disease has significant impacts on affected communities, including morbidity and social stigma.

OBJECTIVE

Investigate the association between environmental factors and the incidence of CL in the province of Ghardaïa and assess the predictive capacity of these factors for disease occurrence.

DESIGN

Retrospective SETTING: The study area included both urban and rural communities.

METHODS

We analyzed a dataset on CL in the province of Ghardaïa, Algeria, spanning from 2000 to 2020. The dataset included climatic variables such as temperature, average humidity, wind speed, rainfall, and the normalized difference vegetation index (NDVI). Using generalized additive models, we examined the relationships and interactions between these variables to predict the emergence of CL in the study area.

MAIN OUTCOME MEASURES

The identification of the most significant environmental factors associated with the incidence and the predicted incidence rates of CL in the province of Ghardaïa, Algeria.

SAMPLE SIZE AND CHARACTERISTICS

252 monthly observations of both climatic and epidemiological variables.

RESULTS

Relative humidity and wind speed were the primary climatic factors influencing the occurrence of CL epidemics in Ghardaïa, Algeria. Additionally, NDVI was a significant environmental factor associated with CL incidence. Surprisingly, temperature did not show a strong effect on CL occurrence, while rainfall was not statistically significant. The final fitted model predictions were highly correlated with real cases.

CONCLUSION

This study provides a better understanding of the long-term trend in how environmental and climatic factors contribute to the emergence of CL. Our results can inform the development of effective early warning systems for preventing the transmission and emergence of vector-borne diseases.

LIMITATIONS

Incorporating additional reservoir statistics such as rodent density and a human development index in the region could improve our understanding of disease transmission.

Seasonal Patterns of Zoonotic Cutaneous Leishmaniasis Caused by L. major and Transmitted by Phlebotomus papatasi in the North Africa Region, a Systematic Review and a Meta-Analysis

BACKGROUND

In North African countries, zoonotic cutaneous leishmaniasis (ZCL) is a seasonal disease linked to Phlebotomus papatasi, Scopoli, 1786, the primary proven vector of L. major dynamics. Even if the disease is of public health importance, studies of P. papatasi seasonal dynamics are often local and dispersed in space and time. Therefore, a detailed picture of the biology and behavior of the vector linked with climatic factors and the framework of ZCL outbreaks is still lacking at the North African countries' level. Our study aims to fill this gap via a systematic review and meta-analysis of the seasonal incidence of ZCL and the activity of P. papatasi in North African countries. We address the relationship between the seasonal number of declared ZCL cases, the seasonal dynamic of P. papatasi, and climatic variables at the North African region scale.

METHODS

We selected 585 publications, dissertations, and archives data published from 1990 to July 2022. The monthly incidence data of ZCL were extracted from 15 documents and those on the seasonal dynamic of P. papatasi from 11 publications from four North African countries.

RESULTS

Our analysis disclosed that for most studied sites, the highest ZCL incidence is recorded from October to February (the hibernal season of the vector), while the P. papatasi density peaks primarily during the hot season of June to September. Overall, at the North African region scale, two to four months laps are present before the apparition of the scars reminiscent of infection by L. major.

CONCLUSIONS

Such analysis is of interest to regional decision-makers for planning control of ZCL in North African countries. They can also be a rationale on which future field studies combining ZCL disease incidence, vector activity, and climatic data can be built.

Environmental, Climatic, and Parasite Molecular Factors Impacting the Incidence of Cutaneous Leishmaniasis Due to Leishmania tropica in Three Moroccan Foci

Cutaneous leishmaniasis (CL) occurring due to Leishmania tropica is a public health problem in Morocco. The distribution and incidence of this form of leishmaniasis have increased in an unusual way in the last decade, and the control measures put in place are struggling to slow down the epidemic. This study was designed to assess the impact of climatic and environmental factors on CL in L. tropica foci. The data collected included CL incidence and climatic and environmental factors across three Moroccan foci (Foum Jemaa, Imintanout, and Ouazzane) from 2000 to 2019. Statistical analyses were performed using the linear regression model. An association was found between the occurrence of CL in Imintanout and temperature and humidity (r² = 0.6076, df = (1.18), p-value = 3.09 × 10⁻⁵; r² = 0.6306, df = (1.18), p-value = 1.77 × 10⁻⁵). As a second objective of our study, we investigated the population structure of L.tropica in these three foci, using the nuclear marker internal transcribed spacer 1 (ITS1). Our results showed a low-to-medium level of geographic differentiation among the L.tropica populations using pairwise differentiation. Molecular diversity indices showed a high genetic diversity in Foum Jemaa and Imintanout; indeed, 29 polymorphic sites were identified, leading to the definition of 13 haplotypes. Tajima’s D and Fu’s F test statistics in all populations were not statistically significant, and consistent with a population at drift–mutation equilibrium. Further analysis, including additional DNA markers and a larger sample size, could provide a more complete perspective of L. tropica’s population structure in these three regions. In addition, further research is needed to better understand the impact of climatic conditions on the transmission cycle of Leishmania, allowing both for the development of effective control measures, and for the development of a predictive model for this parasitosis.

A Review on Climate, Air Pollution, and Health in North Africa

PURPOSE OF REVIEW

The aim of this review is to summarize and provide clear insights into studies that evaluate the interaction between air pollution, climate, and health in North Africa.

RECENT FINDINGS

Few studies have estimated the effects of climate and air pollution on health in North Africa. Most of the studies highlighted the evidence of the link between climate and air pollution as driving factors and increased mortality and morbidity as health outcomes. Each North African country prioritized research on a specific health factor. It was observed that the health outcome from each driving factor depends on the studied area and data availability. The latter is a major challenge in the region. As such, more studies should be led in the future to cover more areas in North Africa and when more data are available. Data availability will help to explore the applicability of different tools and techniques new to the region. This review explores studies related to climate and air pollution, and their possible impacts on health in North Africa. On one hand, air quality studies have focused mainly on particulate matter exceedance levels and their long-term exposure impacts, namely, morbidity and mortality. The observed differences between the various studies are mainly due to the used exposure-response function, the studied population, background emissions, and natural emission from the Sahara Desert that characterize the region. On the other hand, climate studies have focused primarily on the impact of heat waves, vector-borne disease, and mental disorders. More than half of these studies have been on leishmaniasis disease. The review revealed unbalanced and insufficient research on health impacts from air pollution episodes and climate extremes across the region.

Ecological characterization of a cutaneous leishmaniasis outbreak through remotely sensed land cover changes

In this work we assessed the environmental factors associated with the spatial distribution of a cutaneous leishmaniasis (CL) outbreak during 2015-2016 in north-eastern Argentina to understand its typical or atypical eco-epidemiological pattern. We combined locations of human CL cases with relevant predictors derived from analysis of remote sensing imagery in the framework of ecological niche modelling and trained MaxEnt models with cross-validation for predictors estimated at different buffer areas relevant to CL vectors (50 and 250 m radii). To account for the timing of biological phenomena, we considered environmental changes occurring in two periods, 2014-2015 and 2015-2016. The remote sensing analysis identified land cover changes in the surroundings of CL cases, mostly related to new urbanization and flooding. The distance to such changes was the most important variable in most models. The weighted average map denoted higher suitability for CL in the outskirts of the city of Corrientes and in areas close to environmental changes. Our results point to a scenario consistent with a typical CL outbreak, i.e. changes in land use or land cover are the main triggering factor and most affected people live or work in border habitats.

Determination of the trend of incidence of cutaneous leishmaniasis in Kerman province 2014-2020 and forecasting until 2023. A time series study

Introduction

Cutaneous leishmaniasis (CL) is currently a health problem in several parts of Iran, particularly Kerman. This study was conducted to determine the incidence and trend of CL in Kerman during 2014–2020 and its forecast up to 2023. The effects of meteorological variables on incidence was also evaluated.

Materials and methods

4993 definite cases of CL recorded from January 2014 to December 2020 by the Vice-Chancellor for Health at Kerman University of Medical Sciences were entered. Meteorological variables were obtained from the national meteorological site. The time series SARIMA methods were used to evaluate the effects of meteorological variables on CL.

Results

Monthly rainfall at the lag 0 (β = -0.507, 95% confidence interval:-0.955,-0.058) and monthly sunny hours at the lag 0 (β = -0.214, 95% confidence interval:-0.308,-0.119) negatively associated with the incidence of CL. Based on the Akaike information criterion (AIC) the multivariable model (AIC = 613) was more suitable than univariable model (AIC = 690.66) to estimate the trend and forecast the incidence up to 36 months.

Conclusion

The decreasing pattern of CL in Kerman province highlights the success of preventive, diagnostic and therapeutic interventions during the recent years. However, due to endemicity of disease, extension and continuation of such interventions especially before and during the time periods with higher incidence is essential.

Cutaneous leishmaniasis (CL) is one of the most prevalent tropical diseases and the most common form of leishmaniasis, which is found in different regions. Due to different geographical climates, the transmission pattern and the impact of meteorological variables on CL is different. In this study we evaluated the incidence and trend of CL during 2014–2020 and its forecast up to 2023 in Kerman province, Iran. In addition, the impact of meteorological variables on its incidence was assessed. Our finding showed a decreasing trend of CL during the studied years. There was a negative association between CL and sunny hours per day and rainfall at lag 0.

Geographic distribution of Meriones shawi and Psammomys obesus, the main reservoirs and Phlebotomus papatasi vector of zoonotic cutaneous leishmaniasis in Middle East and North Africa

Rodents play a significant role in the balance of a terrestrial ecosystem; they are considered prey for many predators like owls and snakes. However, they present a high risk to agriculture (damaging crops) and health. These rodents are the main reservoirs of some vector-borne diseases like leishmaniasis. Meriones shawi (MS) and Psammomys obesus (PO) are the primary Zoonotic cutaneous leishmaniasis (ZCL) reservoirs in the Middle East and North Africa (MENA). A review on the MS and PO at the MENA scale was explored. A database of about 1500 papers was used. 38 sites were investigated as foci for MS and 36 sites for PO, and 83 sites of Phlebotomus papatasi (Pp) in the studied region. An updated map at the regional scale and the trend of the reservoir distribution was carried out using a performing proper density analysis. In this paper, climatic conditions and habitat characteristics of these two reservoirs were reviewed. The association of rodent density with some climatic variables is another aspect explored in a case study from Tunisia in the period 2009–2015 using Pearson correlation. Lastly, the protection and control measures of the reservoir were analyzed. The high concentration of the MS, PO, and Pp can be used as an indicator to identify the high-risk area of leishmaniasis infection.

The effect and prediction of diurnal temperature range in high altitude area on outpatient and emergency room admissions for cardiovascular diseases

PURPOSE

Diurnal temperature range (DTR) is a meteorological indicator closely associated with global climate change. Thus, we aim to explore the effects of DTR on the outpatient and emergency room (O&ER) admissions for cardiovascular diseases (CVDs), and related predictive research.

METHODS

The O&ER admissions data for CVDs from three general hospitals in Jinchang of Gansu Province were collected from 2013 to 2016. A generalized additive model (GAM) with Poisson regression was employed to analyze the effect of DTR on the O&ER admissions for all cardiovascular diseases, hypertension, ischemic heart disease (IHD) and stoke. GAM was also used to preform predictive research of the effect of DTR on the O&ER admissions for CVDs.

RESULTS

There were similar positive linear relationships between DTR and the O&ER visits with the four cardiovascular diseases. And the cumulative lag effects were higher than the single lag effects. A 1 °C increase in DTR corresponded to a 1.30% (0.99-1.62%) increase in O&ER admissions for all cardiovascular diseases. Males and elderly were more sensitivity to DTR. The estimates in non-heating season were higher than in heating season. The trial prediction accuracy rate of CVDs based on DTR was between 59.32 and 74.40%.

CONCLUSIONS

DTR has significantly positive association with O&ER admissions for CVDs, which can be used as a prediction index of the admissions of O&ER with CVDs.

The effect of climate variables on the incidence of cutaneous leishmaniasis in Isfahan, Central Iran

In recent years, there have been considerable changes in the distribution of diseases that are potentially tied to ongoing climate variability. The aim of this study was to investigate the association between the incidence of cutaneous leishmaniasis (CL) and climatic factors in an Iranian city (Isfahan), which had the highest incidence of CL in the country. CL incidence and meteorological data were acquired from April 2010 to March 2017 (108 months) for Isfahan City. Univariate and multivariate seasonal autoregressive integrated moving average (SARIMA), generalized additive models (GAM), and generalized additive mixed models (GAMM) were used to identify the association between CL cases and meteorological variables, and forecast CL incidence. AIC, BIC, and residual tests were used to test the goodness of fit of SARIMA models; and R² was used for GAM/GAMM. 6798 CL cases were recorded during this time. The incidence had a seasonal pattern and the highest number of cases was recorded from August to October. In univariate SARIMA, (1,0,1) (0,1,1)₁₂ was the best fit for predicting CL incidence (AIC=8.09, BIC=8.32). Time series regression (1,0,1) (0,1,1)₁₂ showed that monthly mean humidity after 4-month lag was inversely related to CL incidence (AIC=8.53, BIC=8.66). GAMM results showed that average temperature with 2-month lag, average relative humidity with 3-month lag, monthly cumulative rainfall with 1-month lag, and monthly sunshine hours with 1-month lag were related to CL incidence (R²=0.94). The impact of meteorological variables on the incidence of CL is not linear and GAM models that include non-linear structures are a better fit for prediction. In Isfahan, Iran, meteorological variables can greatly predict the incidence of CL, and these variables can be used for predicting outbreaks.

Association between particulate matter pollution and the incidence of mumps in 31 provinces from China

Previous studies have found that particulate matter (PM) pollution is a risk factor for respiratory disease by affecting body's immunity and carrying microorganisms. This study aimed to explore the association between PM and the incidence of mumps in 31 provinces from China. Monthly mumps cases, air pollution concentration, and meteorological factors in each province were obtained between January 2014 to December 2017. We used a generalized additive model (GAM) to investigate the associations of PM2.5 and PM10 with monthly mumps cases. We also tested the statistical significance of the differences between effect estimates in the warm season (April to September) and cold season (October to March) to explore potential effect modification. We found that a 10-μg/m3 increase (lag0) in PM2.5, and PM10 was associated with a 2.34% (95% CI: 1.32 to 3.36) and 1.90% (95% CI: 1.19 to 2.62) increase in the monthly counts of mumps cases, respectively. We also observed significant positive associations of PM2.5 and PM10 with mumps cases at lag0-1. These results were robust in our sensitivity analyses. No significant differences were found between the season-specific effects. Our results indicate that there is a positive relationship between PM and the incidence of mumps, which provides important implications for the prevention and control of mumps.

Association between meteorological factors and daily new cases of COVID-19 in 188 countries: A time series analysis

By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and environment are essential factors to consider in transmission. We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM). Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under -23°C at lag ten days, the minimum RR of wind speed was 0.29 under 12m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust. As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.

COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response

Background

Little attention has been paid to the comparison of COVID-19 pandemic responses and related factors in BRICS (Brazil, Russia, India, China, and South Africa) countries. We aimed at evaluating the association of daily new COVID-19 cases with socio-economic and demographic factors, health vulnerability, resources, and policy response in BRICS countries.

Methods

We conducted a cross-sectional study using data on the COVID-19 pandemic and other indicators of BRICS countries from February 26, 2020 to April 30, 2021. We compared COVID-19 epidemic in BRICS countries and analyzed related factors by log-linear Generalized Additive Model (GAM) models.

Results

In BRICS countries, India had the highest totally of confirmed cases with 18.76 million, followed by Brazil (14.45 million), Russia (4.81 million), and South Africa (1.58 million), while China (0.10 million) had the lowest figure. South Africa had the lowest rate of administered vaccine doses (0.18 million) among BRICS countries as of April 30, 2021. In the GAM model, a 1 unit increase in population density and policy stringency index was associated with a 5.17% and 1.95% growth in daily new COVID-19 cases (P < 0.001), respectively. Exposure–response curves for the effects of policy stringency index on daily new cases showed that there was a rapid surge in number of daily new COVID-19 cases when the index ranged from 0 to 45. The number of infections climbed slowly when the index ranged from 46 to 80, and decreased when the index was above 80 (P < 0.001). In addition, daily new COVID-19 cases (all P < 0.001) were also correlated with life expectancy at birth (-1.61%), extreme poverty (8.95%), human development index (-0.05%), GDP per capita (-0.18%), diabetes prevalence (0.66%), proportion of population aged 60 and above (2.23%), hospital beds per thousand people (-0.08%), proportion of people with access to improved drinking water (-7.40%), prevalence of open defecation (0.69%), and annual tourist/visitor arrivals (0.003%), after controlling other confounders. Different lag structures showed similar results in the sensitivity analysis.

Conclusions

Strong policy response is crucial to control the pandemic, such as effective containment and case management. Our findings also highlighted the importance of reducing socio-economic inequalities and strengthening the resilience of health systems to better respond to public health emergencies globally.

Graphic abstract

Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data

Pogosta disease is a mosquito-borne infection, caused by Sindbis virus (SINV), which causes epidemics of febrile rash and arthritis in Northern Europe and South Africa. Resident grouse and migratory birds play a significant role as amplifying hosts and various mosquito species, including Aedes cinereus, Culex pipiens, Cx. torrentium and Culiseta morsitans are documented vectors. As specific treatments are not available for SINV infections, and joint symptoms may persist, the public health burden is considerable in endemic areas. To predict the environmental suitability for SINV infections in Finland, we applied a suite of geospatial and statistical modeling techniques to disease occurrence data. Using an ensemble approach, we first produced environmental suitability maps for potential SINV vectors in Finland. These suitability maps were then combined with grouse densities and environmental data to identify the influential determinants for SINV infections and to predict the risk of Pogosta disease in Finnish municipalities. Our predictions suggest that both the environmental suitability for vectors and the high risk of Pogosta disease are focused in geographically restricted areas. This provides evidence that the presence of both SINV vector species and grouse densities can predict the occurrence of the disease. The results support material for public-health officials when determining area-specific recommendations and deliver information to health care personnel to raise awareness of the disease among physicians.

A review of the COVID-19 pandemic and its interaction with environmental media

Viruses are biologically active parasites that only exist inside a host they are submicroscopic level. The novel coronavirus disease, or COVID-19, is generally caused by the SARS-CoV-2 virus and is comparable to severe acute respiratory syndrome (SARS). As a result of globalization, natural alterations or changes in the SARS-CoV-2 have created significant risks to human health over time. These viruses can live and survive in different ways in the atmosphere unless they reach another host body. At this stage, we will discuss the details of the transmission and detection of this deadly SARS-CoV-2 virus via certain environmental media, such as the atmosphere, water, air, sewage water, soil, temperature, relative humidity, and bioaerosol, to better understand the diffusion, survival, infection potential and diagnosis of COVID-19.

An investigation of the effects of environmental and ecologic factors on cutaneous leishmaniasis in the old world: a systematic review study

OBJECTIVES

Leishmaniasis is a neglected and widespread parasitic disease that can lead to serious health problems. The current review study aimed to synthesize the relationship between ecologic and environmental factors (e.g., weather conditions, climatology, temperature and topology) and the incidence of cutaneous leishmaniasis (CL) in the Old World.

CONTENT

A systematic review was conducted based on English, and Persian articles published from 2015 to 2020 in PubMed/Medline, Science Direct, Web of Science and Google Scholar. Keywords used to search articles were leishmaniasis, environmental factors, weather condition, soil, temperature, land cover, ecologic* and topogr*. All articles were selected and assessed for eligibility according to the titles or abstracts. The quality screening process of articles was carried out by two independent authors. The selected articles were checked according to the inclusion and exclusion criteria.

SUMMARY AND OUTLOOK

A total of 827 relevant records in 2015-2020 were searched and after evaluating the articles, 23 articles met the eligibility criteria; finally, 14 full-text articles were included in the systematic review. Two different categories of ecologic/environmental factors (weather conditions, temperature, rainfall/precipitation and humidity) and land characteristics (land cover, slope, elevation and altitude, earthquake and cattle sheds) were the most important factors associated with CL incidence.

CONCLUSIONS

Temperature and rainfall play an important role in the seasonal cycle of CL as many CL cases occurred in arid and semiarid areas in the Old World. Moreover, given the findings of this study regarding the effect of weather conditions on CL, it can be concluded that designing an early warning system is necessary to predict the incidence of CL based on different weather conditions.

How the global health security index and environment factor influence the spread of COVID-19: A country level analysis

The progress of viral diseases such as the new coronavirus (COVID-19) can be influenced not only by social isolation policies, but also by climatic factors. Understanding how these factors affect the progress of the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be essential to know the risks each country is facing because of the disease. In this study, we verified the existence of a relationship between the basic reproduction number (R0) of SARS-CoV-2 with different climate variables, while also considering the Global Health Security Index (GHS). We collected data from confirmed cases of COVID-19 along with their respective GHS notes and climate data, from December 31, 2019 to April 13, 2020, for 52 countries. The generalized additive model (GAM) was applied to explore the effect of temperature, relative humidity, solar radiation index, and GHS score on the spread rate of COVID-19. The countries that showed similarity to each other were grouped into clusters using the Kohonen self-organizing map methodology to investigate the importance of each variable in the dissemination of the disease. The temperature variable presented a linear relationship (p < 0.001) with the R0, with an explained variation of 36.2%, while the relative humidity variable did not present a significant relationship with the R0. The response curve of the solar radiation variable presented a significant nonlinear relationship (p < 0.001) with an explained variation of 32.3%. The GHS index variable, with a significant nonlinear relationship (p < 0.001), presented the largest explanatory response in the control of COVID-19, with an explained variation of 38.4%; further, it was observed that the countries with the largest GHS index scores were less influenced by climate variables.

Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis

BACKGROUND

Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series.

AIM

To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil.

METHODS

A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box-Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020.

RESULTS

A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020.

LIMITATIONS

This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported.

CONCLUSION

The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries

Meteorological parameters are the critical factors of affecting respiratory infectious disease such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and influenza, however, the effect of meteorological parameters on coronavirus disease 2019 (COVID-19) remains controversial. This study investigated the effects of meteorological factors on daily new cases of COVID-19 in 127 countries, as of August 31 2020. The log-linear generalized additive model (GAM) was used to analyze the effect of meteorological variables on daily new cases of COVID-19. Our findings revealed that temperature, relative humidity, and wind speed are nonlinearly correlated with daily new cases, and they may be negatively correlated with the daily new cases of COVID-19 over 127 countries when temperature, relative humidity and wind speed were below 20°C, 70% and 7 m/s respectively. Temperature(>20°C) was positively correlated with daily new cases. Wind speed (when>7 m/s) and relative humidity (>70%) was not statistically associated with transmission of COVID-19. The results of this research will be a useful supplement to help healthcare policymakers in the Belt and Road countries, the Centers for Disease Control (CDC) and the World Health Organization (WHO) to develop strategies to combat COVID-19.

Visceral leishmaniasis in northwest China from 2004 to 2018: a spatio-temporal analysis

BACKGROUND

Although visceral leishmaniasis (VL), a disease caused by parasites, is controlled in most provinces in China, it is still a serious public health problem and remains fundamentally uncontrolled in some northwest provinces and autonomous regions. The objective of this study is to explore the spatial and temporal characteristics of VL in Sichuan Province, Gansu Province and Xinjiang Uygur Autonomous Region in China from 2004 to 2018 and to identify the risk areas for VL transmission.

METHODS

Spatiotemporal models were applied to explore the spatio-temporal distribution characteristics of VL and the association between VL and meteorological factors in western China from 2004 to 2018. Geographic information of patients from the National Diseases Reporting Information System operated by the Chinese Center for Disease Control and Prevention was defined according to the address code from the surveillance data.

RESULTS

During our study period, nearly 90% of cases occurred in some counties in three western regions (Sichuan Province, Gansu Province and Xinjiang Uygur Autonomous Region), and a significant spatial clustering pattern was observed. With our spatiotemporal model, the transmission risk, autoregressive risk and epidemic risk of these counties during our study period were also well predicted. The number of VL cases in three regions of western China concentrated on a few of counties. VL in Kashi Prefecture, Xinjiang Uygur Autonomous Region is still serious prevalent, and integrated control measures must be taken in different endemic areas.

CONCLUSIONS

The number of VL cases in three regions of western China concentrated on a few of counties. VL in Kashi Prefecture, Xinjiang Uygur Autonomous Region is still serious prevalent, and integrated control measures must be taken in different endemic areas. Our findings will strengthen the VL control programme in China.

Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

BACKGROUND

Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia.

METHODS

We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt.

RESULTS

Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73-3.69] to 1.77 [95% CrI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84-0.94]) by national lockdown measure.

CONCLUSIONS

Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

Spatio-temporal modeling of visceral leishmaniasis in Midwest Brazil: An ecological study of 18-years data (2001-2018)

Visceral leishmaniasis (VL) is a neglected vector-borne disease associated with socioeconomic and environmental issues. In Brazil, epidemics of VL have occurred in major cities since 1980. Applied models for medical and epidemiological research have been used to assess the distribution and characteristics of disease endpoints and identify and characterize potential risk factors. This study described the demographic features of VL and modeled the spatio-temporal distribution of human VL cases and their relationship with underlying predicitve factors using generalized additive models. We conducted an ecological study covering an 18-year period from the first report of an autochthonous case of VL in Campo Grande, state of Mato Grosso do Sul, in 2001 to 2018. The urban area of the city has 74 neighborhoods, and they were the units of analysis of our work. Socioeconomic and demographic data available from Brazilian public databases were considered as covariables. A total of 1,855 VL cases were reported during the study period, with an annual mean incidence rate of 13.23 cases per 100,000 population and a cumulative crude incidence of 235.77 per 100,000 population. The results showed the rapid transition from epidemic to endemic and the centrifugal dispersal pattern of the disease. Moreover, the model highlighted that the urban quality of life index, which is calculated based on income, education, housing conditions, and environmental sanitation data, plays a role in VL occurrence. Our findings highlighted the potential for improving spatio-temporal segmentation of control measures and the cost-effectiveness of integrated disease management programs as soon as VL is difficult to control and prevent and has rapid geographical dispersion and increased incidence rates.

Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China

The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM2.5, PM10, SO2, CO, NO2 and O3) with COVID-19 confirmed cases. We observed significantly positive associations of PM2.5, PM10, NO2 and O3 in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m3 increase (lag0-14) in PM2.5, PM10, NO2, and O3 was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m3 increase (lag0-14) in SO2 was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.

Association between ambient temperature and COVID-19 infection in 122 cities from China

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.

METHODS

Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.

RESULTS

The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.

CONCLUSIONS

Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Association between ambient temperature and COVID-19 infection in 122 cities from China

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.

METHODS

Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.

RESULTS

The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.

CONCLUSIONS

Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Environmental and socioeconomic risk factors associated with visceral and cutaneous leishmaniasis: a systematic review

We performed a systematic review of the literature published since 1900 about leishmaniasis a neglected vector-borne disease, focused on environmental and social risk factors for visceral (VL) and cutaneous leishmaniasis (CL) to better understand their impact on the incidence of disease. The search terms were "leishmaniasis" AND "risk factors" using Google Scholar, PudMed, and Scielo. We reviewed 177 articles, 95 studies for VL, 75 for CL, and 7 on both forms. We identified 14 categories of risk factors which were divided into three groups: socioeconomic (7), environmental (5), and climate (2) variables. Socioeconomic factors were also associated with disease incidence in vulnerable human populations of arid and tropical developing regions. Environmental and climate factors showed significant associations with the incidence of VL and CL in all the studies that considered them. Proximity to natural vegetation remnants increased disease risk in both the New and Old World while the climate conditions favorable for disease transmission differed among regions. We propose a common conceptual framework for both clinical forms that highlights networks of interaction among risk factors. In both clinical forms, the interplay of these factors played a major role in disease incidence. Although there are similarities in environmental and socioeconomic conditions that mediate the transmission cycle of tropical, arid, and Mediterranean regions, the behavior of vector and reservoirs in each region is different. Special attention should be given to the possibility of vector adaptation to urban environments in developing countries where populations with low socioeconomic status are particularly vulnerable to the disease.

Epidemiological and time series analysis of haemorrhagic fever with renal syndrome from 2004 to 2017 in Shandong Province, China

Shandong Province is an area of China with a high incidence of haemorrhagic fever with renal syndrome (HFRS); however, the general epidemic trend of HFRS in Shandong remains unclear. Therefore, we established a mathematical model to predict the incidence trend of HFRS and used Joinpoint regression analysis, a generalised additive model (GAM), and other methods to evaluate the data. Incidence data from the first half of 2018 were included in a range predicted by a modified sum autoregressive integrated moving average-support vector machine (ARIMA-SVM) combination model. The highest incidence of HFRS occurred in October and November, and the annual mortality rate decreased by 7.3% (p < 0.05) from 2004 to 2017. In cold months, the incidence of HFRS increased by 4%, −1%, and 0.8% for every unit increase in temperature, relative humidity, and rainfall, respectively; in warm months, this incidence changed by 2%, −3%, and 0% respectively. Overall, HFRS incidence and mortality in Shandong showed a downward trend over the past 10 years. In both cold and warm months, the effects of temperature, relative humidity, and rainfall on HFRS incidence varied. A modified ARIMA-SVM combination model could effectively predict the occurrence of HFRS.

Effects of time-lagged meteorological variables on attributable risk of leishmaniasis in central region of Afghanistan

BACKGROUND

Leishmaniasis remains one of the world's most neglected vector-borne diseases, affecting predominantly poor communities mainly in developing countries. Previous studies have shown that the distribution and dynamics of leishmaniasis infections are sensitive to environmental factors, however, there are no studies on the burden of leishmaniasis attributable to time-varying meteorological variables.

METHODS

This study used data from 3 major leishmaniosis afflicted provinces of Afghanistan, between 2003 and 2009, to provide empirical analysis of change in heat/cold-leishmaniosis association. Non-linear and delayed exposure-lag-response relationship between meteorological variables and leishmaniasis were fitted with a distributed lag non-linear model applying a spline function which describes the dependency along the range of values with a lag of up to 12 months. We estimated the risk of leishmaniasis attributable to high and low temperature.

RESULTS

The median monthly mean temperature and rainfall were 16.1 °C and 0.6 in., respectively. Seasonal variations of leishmaniasis were consistent between males and females, however significant differences were observed among different age groups. Temperature effects were immediate and persistent (lag 0-12 months). The cumulative risks were highest at cold temperatures. The cumulative relative risks (logRR) for leishmaniasis were 6.16 (95% CI: 5.74-6.58) and 1.15 (95% CI: 1.32-1.31) associated with the 10th percentile temperature (2.16 °C) and the 90th percentile temperature (28.46 °C). The subgroup analysis showed increased risk for males as well as young and middle aged people at cold temperatures, however, higher risk was observed for the elderly in heat. The overall leishmaniasis-temperature attributable fractions was estimated to be 7.6% (95% CI: 7.5%-7.7%) and mostly due to cold.

CONCLUSION

Findings in this study highlight the non-linearity, delay of effects and magnitude of leishmaniasis risk associated with temperature. The disparity of risk between different subgroups can hopefully advise policy makers and assist in leishmaniasis control program.

Associations between Meteorological Factors and Visceral Leishmaniasis Outbreaks in Jiashi County, Xinjiang Uygur Autonomous Region, China, 2005-2015

Although visceral leishmaniasis disease is controlled overall in China, it remains a serious public health problem and remains fundamentally uncontrolled in Jiashi County, Xinjiang Uygur Autonomous Region. During 2005–2015, there were two outbreaks in Jiashi County. Assessing the influence of meteorological factors on visceral leishmaniasis incidence is essential for its monitoring and control. In this study, we applied generalized estimating equations to assess the impact of meteorological factors on visceral leishmaniasis risk from 2005 to 2015. We also compared meteorological factors among years with Kruskal–Wallis test to explore possible reasons behind the two outbreaks that occurred during our study period. We found that temperature and relative humidity had very significant associations with visceral leishmaniasis risk and there were interactions between these factors. Increasing temperature or decreasing relative humidity could increase the risk of visceral leishmaniasis events. The outbreaks investigated might have been related to low relative humidity and high temperatures. Our findings will support the rationale for visceral leishmaniasis control in China.

Modeling spatial risk of zoonotic cutaneous leishmaniasis in Central Iran

Zoonotic Cutaneous Leishmaniasis (ZCL) is one of the endemic diseases in central part of Iran. The aim of this cross-sectional study was to find the areas with a higher risk of infection considering the distribution of vector, reservoir hosts and human infection. Passive data recorded the positive cases of cutaneous leishmaniasis in Yazd province health center were collected for 10 years, from 2007 to 2016 at the County level. Considering all earlier studies conducted in Yazd province, records of Phlebotomus papatasi, the main vector of ZCL, and Rhombomys opimus, the main reservoir of ZCL, were collected and entered in a database. ArcGIS and MaxEnt model were used to map and predict the best ecological niches for both vector and reservoir. The most cumulative incidence of the disease was found to be in Khatam County, south of Yazd province. The area under curve (AUC) for R. opimus and P. papatasi was 0.955 and 0.914, respectively. We found higher presence probability of both vector and reservoir in central and eastern parts of the province. The jackknife test indicated that temperature and normalized difference vegetation index (NDVI) had the most effect on the model for the vector and reservoir, respectively. The areas with higher presence probability for the reservoirs and vectors were considered having the higher potential for ZCL transmission. These findings can be used to prevent and control the disease.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic

OBJECTIVES

This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.

METHODS

This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely highand low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.

RESULTS

A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p<0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p<0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p<0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p<0.001).

CONCLUSIONS

This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.