Climate variability and infectious diseases nexus: Evidence from Sweden

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.

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.

Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014

BACKGROUND

Infections with nontyphoidal Salmonella cause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence of Salmonella in soil and food. However, the impact of extreme weather events on Salmonella infection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions.

METHODS

To address this knowledge gap, we obtained Salmonella case data for S. Enteriditis, S. Typhimurium, S. Newport, and S. Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95^th percentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates.

RESULTS

We observed that extreme heat exposure was associated with increased rates of infection with S. Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates of S. Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate of S. Newport infections in Maryland associated with extreme precipitation events.

CONCLUSIONS

Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection with Salmonella serovars that persist in environmental or plant-based reservoirs, such as S. Javiana and S. Newport, appear to be of particular significance regarding increased heat and rainfall events.

Association between environmental factors and COVID-19 in Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system. The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors. We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21, 2020, to February 29, 2020, in Shanghai, China. We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (Tave), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM2.5, PM10 NO2, and SO2 were significantly associated with the increase in daily confirmed COVID-19 cases. A 1 °C increase in Tave, one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7, 1.8, and 1.6% decrease in daily confirmed cases, respectively. Daily Tave, THI, K, PM10, and SO2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM2.5, and NO2 were significant at both lag 0-7 and 0-14 days. A 10-μg/m3 increase in PM10 and 1-μg/m3 increase in SO2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-μg/m3 increase in PM2.5 and NO2 were associated with 7.9, 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively. Our findings have important implications for public health in the city of Shanghai.

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.

Meteorological factors, governmental responses and COVID-19: Evidence from four European countries

With the global lockdown, meteorological factors are highly discussed for COVID-19 transmission. In this study, national-specific and region-specific data sets from Germany, Italy, Spain and the United Kingdom were used to explore the effect of temperature, absolute humidity and diurnal temperature range (DTR) on COVID-19 transmission. From February 1st to November 1st, a 7-day COVID-19 case doubling time (Td), meteorological factors with cumulative 14-day-lagged, government response index and other factors were fitted in the distributed lag nonlinear models. The overall relative risk (RR) of the 10th and the 25th percentiles temperature compared to the median were 0.0074 (95% CI: 0.0023, 0.0237) and 0.1220 (95% CI: 0.0667, 0.2232), respectively. The pooled RR of lower (10th, 25th) and extremely high (90th) absolute humidity were 0.3266 (95% CI: 0.1379, 0.7734), 0.6018 (95% CI: 0.4693, 0.7718) and 0.3438 (95% CI: 0.2254, 0.5242), respectively. While the DTR did not have a significant effect on Td. The total cumulative effect of temperature (10th) and absolute humidity (10th, 90th) on Td increased with the change of lag days. Similarly, a decline in temperature and absolute humidity at cumulative 14-day-lagged corresponded to the lower RR on Td in pooled region-specific effects. In summary, the government responses are important factors in alleviating the spread of COVID-19. After controlling that, our results indicate that both the cold and the dry environment also likely facilitate the COVID-19 transmission.

Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model

This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. GAM depicts that a 1 μg/m³, 1 μg/m³, and 1 ppm increase (lag 0-7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI: 7.01 to -2.00) decrease, 1.62% (CI: 2.23 to -1.022) decrease, and 4.66% (CI: 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.

Effects of heavy rainfall on waterborne disease hospitalizations among young children in wet and dry areas of New Zealand

Heavy rainfall is associated with increased risk of waterborne disease. However, it is not known whether the risk increment differs between wet and dry regions. We examined this question in New Zealand, which has a wide geographical variation of annual rainfall totals (10^th-90^th percentile difference ≥3000 mm). We conducted a nested case-crossover study within a prospective child cohort (born in 2009-2010) for assessing transient health effects when modified by longitudinal exposures to rainfall. Short-term heavy rainfall effects on hospitalizations due to enteric bacterial and viral infectious causes at lag of 0-14 days were assessed using a Cox regression model adjusted for daily temperature, relative humidity and evapotranspiration. We derived quantiles of time-weighted long-term rainfall levels at the children's homes and these were added as an interaction term to the short-term effect model. Hospitalization risks were higher two days after heavy rainfall days (hazard ratio [95% confidence interval]: 1.73 [1.10-2.70]). The lowest-observable-adverse-effect-level was detected at the 94^th percentile of daily rainfall total. Hospital admissions 1-2 days after heavy rainfall increased most in locations with the lowest and highest long-term rainfall. An interaction of this kind between short-term weather and long-term climate has not been reported previously. It is relevant to climate change risk assessments given global projections of increasing intensity of precipitation, against a background of more severe, and possibly more frequent, droughts and flooding.

The spread of 2019-nCoV in China was primarily driven by population density. Comment on "Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China" by Zhu et al

Recently, an article published in the journal Science of the Total Environment and authored by Zhu et al. has claimed the "Association between short-term exposure to air pollution and COVID-19 infection" (doi: https://doi.org/10.1016/j.scitotenv.2020.138704). This note shows that the stated dependence between the diffusion of the infection and air pollution may be the result of spurious correlation due to the omission of a common factor, namely, population density. To this end, the relationship between demographic, socio-economic, and environmental conditions and the spread of the novel coronavirus in China is analyzed with spatial regression models on variables deflated by population size. The infection rate - as measured by the number of cases per 100 thousand inhabitants - is found to be strongly related to the population density. At the same time, the association with air pollution is detected with a negative sign, which is difficult to interpret.

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis

Background

This work presents a forecast model for non-typhoidal salmonellosis outbreaks.

Method

This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA).

Results

The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014–2016), the environmental conditions and the consumption of high-risk food as predictive variables.

Conclusions

The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

Airborne particulate matter, population mobility and COVID-19: a multi-city study in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China.

METHODS

We obtained daily confirmed cases of COVID-19, air particulate matter (PM_2.5, PM₁₀), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM₁₀, PM_2.5 and MSI on daily confirmed COVID-19 cases.

RESULTS

We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m³ increase in the concentration of PM₁₀ and PM_2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM₁₀ and PM_2.5 were at lag 014, and the RRs of each 10 μg/m³ increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively.

CONCLUSIONS

Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

The mediating effect of air quality on the association between human mobility and COVID-19 infection in China

BACKGROUND

Previous studies have found that human mobility restrictions could not only prevent the spread of COVID-19, but also improve the air quality because of the reduction of industrial production, transportation and traffic. It is noteworthy that air quality is also closely related to the risk of COVID-19 infection. Therefore, we aimed to assess the mediating role of air quality on the association between human mobility and the infection caused by this novel coronavirus.

METHODS

We collected daily confirmed cases, human mobility data, air quality data and meteorological variables in 120 cities from China between January 23, 2020 and February 29, 2020. We applied the generalized additive model to examine the association of human mobility index with COVID-19 confirmed cases, and to assess the mediating effects of air quality index and each pollutant.

RESULTS

We observed a significant positive relationship between human mobility index and the daily counts of COVID-19 confirmed cases. A unit increase in human mobility index (lag0-14) was associated with a 6.45% increase in daily COVID-19 confirmed cases, and air quality index significantly mediated 19.47% of this association. We also observed a positive relationship between human mobility index and air quality index. In the pollutant level analyses, we found significant mediating effects of PM2.5, PM10, and NO2.

CONCLUSIONS

Our study suggests that limiting human movements could reduce COVID-19 cases by improving air quality besides decreasing social contact.

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.

Climate Change and Infectious Diseases: Evidence from Highly Vulnerable Countries

BACKGROUND

Climate change is an alarming challenge for humanity at large due to its mediating role in emergence and spread of infectious diseases like cholera and malaria. This study was conducted to examine the effect of climate change and some socio-economic factors on incidence of infectious diseases.

METHODS

We used country level panel data over the 1990-2017 period using panel ARDL-PMG technique on highly affected countries from climate change.

RESULTS

There is a long run co-integrating relationship among climate change, socio-economic factors and prevalence of infectious diseases. Climate change, as measured by the temperature, is contributing to the spread of infectious diseases.

CONCLUSION

This is the first study giving evidence of the impact of climate change on incidence of infectious diseases as can be seen from highly vulnerable countries to climate change. It is recommended to improve the level of education along with public health and town planning to reduce the incidence of infectious diseases.

Spatiotemporal Analysis of Influenza Morbidity and Its Association with Climatic and Housing Conditions in Ecuador

The external environment directly influences human health. However, what happens inside? This work deals with the effect that the interior thermal variables have on the propagation of respiratory diseases and focused on the relation of the temperature and relative humidity inside social housing in the 1040 parishes of Ecuador and the transmission of influenza. On the one hand, historical weather-related variables were used to simulate and estimate the interior conditions, and thresholds on temperature and humidity were determined. On the other hand, the health-related variable was determined by analyzing the statistics corresponding to the influenza and viral pneumonia in 2009 since that year was critical for these diseases; the data were divided by month for each parish. Finally, the correlation of these variables determines the relative importance of the interior conditions on the respiratory health of its inhabitants. The preliminary results indicate that the places with the lowest temperatures and relative humidity could favor the virus transmission. Also, the analysis indicated that respiratory diseases increase in August and October. In this way, it is clear that social housing projects in Ecuador require a study which guarantees not only energy efficiency and sustainability related issues but also the well-being of their inhabitants.

Nexus between Air Pollution and Neonatal Deaths: A Case of Asian Countries

The rapid economic growth in Asian countries has witnessed a persistent increase in air pollution complementing adverse health challenges for children in these countries. Quantification of health effects attributable to air pollution (PM_2.5) is important in policy implications to tackle air pollution and associated health problems. This study aims to explore the nexus between air pollution and neonates’ deaths embedded in acute respiratory infection. We collected panel data from the 12 most vulnerable Asian countries over the period of 2000–2017 and analyzed through the fixed-effect model. Empirical results show a positive relation between air pollution, temperature, and neonates’ deaths in the studied Asian countries. The results have attested negative impacts of income and education while positive effect of population density on neonates’ deaths due to acute respiratory infection. Diagnostic and prognostic measures have checked the pace of the respiratory diseases caused by PM_2.5 and resultant deaths in Asian countries; yet alarming factors, like mounting industrial air pollution and rapid expansion of industrial zones in urban areas, need to be addressed in policy implications for long term sustainable solutions.

Modeling and Predicting Dengue Incidence in Highly Vulnerable Countries using Panel Data Approach

The spread of dengue has become a major public health concern in recent times due to alarming climate change. Using country level panel data over the 2000–2017 period, this paper examines the effects of climate change and socio-economic variables on the incidence of dengue-borne diseases in some of the most highly vulnerable countries. Empirical analysis shows a positive association between climate change and socio-economic conditions in the advent of dengue-borne diseases. We find that climate change, as measured by temperature, is proactively contributing to the spread of dengue-borne diseases. However, redressing the contributive factor behind climate change, via better awareness through education and improved public health facilitation, can assist in managing the occurrences and spread of dengue-borne diseases.

Human infectious diseases and the changing climate in the Arctic

Climatic factors, especially temperature, precipitation, and humidity play an important role in disease transmission. As the Arctic changes at an unprecedented rate due to climate change, understanding how climatic factors and climate change affect infectious disease rates is important for minimizing human and economic costs. The purpose of this systematic review was to compile recent studies in the field and compare the results to a previously published review. English language searches were conducted in PubMed, ScienceDirect, Scopus, and PLOS One. Russian language searches were conducted in the Scientific Electronic Library "eLibrary.ru". This systematic review yielded 22 articles (51%) published in English and 21 articles (49%) published in Russian since 2012. Articles about zoonotic and vector-borne diseases accounted for 67% (n = 29) of the review. Tick-borne diseases, tularemia, anthrax, and vibriosis were the most researched diseases likely to be impacted by climatic factors in the Arctic. Increased temperature and precipitation are predicted to have the greatest impact on infectious diseases in the Arctic.