Current understanding of the airborne transmission of important viral animal pathogens in spreading disease

Current research on airborne transmission of African swine fever virus (ASFV), porcine epidemic diarrhoea virus (PEDV), avian influenza (AIV), porcine reproductive and respiratory syndrome virus (PRRSV), and foot and mouth disease virus (FMDV) was reviewed to evaluate commonalities, knowledge gaps, and methodologies of studying airborne transmission of animal diseases. The reviewed studies were categorised as short-range transmission (within a single facility) and long-range transmission (beyond a single site). Short-range airborne transmission was demonstrated for at least one strain of the above-mentioned pathogens in experimental settings. Most studies reported in the literature concern FMDV, with limited information for ASFV and PEDV, particularly for short-range airborne transmission. Air sampling upwind, downwind, and within infected facilities has been commonly used to demonstrate long-range airborne transmission. The amount of evidence from air sampling for each of the reviewed viruses varies from no evidence on ASFV to evidence from multiple settings for AIV. Computer modelling has been used to study past outbreaks of infectious diseases to assess the contribution of airborne transmission with a multitude of computer models reported in the literature for simulating long-range airborne transmission of FMDV based on past outbreaks. This has resulted in predictive tools for assessing future risk of airborne transmission. Some important computer models are based on epidemiology analysis, weather analysis, and air dispersion. Few models are reported for ASFV, PEDV, and PRRSV. Studies in the literature indicate that airborne transmission is generally affected by virus strain, aerosol type, shedding duration and concentration, environmental conditions, and infectious dose.

Impact of climatic factors on viability of SARS-CoV-2 and transmission prospective of COVID-19: An overview

The emergence of the new coronavirus disease-19 (Covid-19) from exotic wild animal market in Hubei, China during the late December 2019 has spread in 250 countries and territories posing menace to health of people around the globe. Coronavirus disease-19 has a great impact on public health, mortality and economy. The causing agent of Covid-19 is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to unavailability of the potential drug or treatment for Covid-19, multi factorial research is conducted to reduce its transmission. Climatic factors play a key role in regulating the transmission of the infectious diseases like Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory syndrome, and influenza. So, the rate of prevalence of the infectious disease is likely to be inclined by the variation in climatic indicators. We conclude from the past and present experiences that coronavirus transmission is elevated under low temperature and high humidity and vice versa. It further infers that after three hours of incubation of SARS-CoV-2, no infectious virus could be recovered from tissue paper and printing. Smooth surfaces are found to be more favorable for SARS-CoV-2. We may infer that climatic variations greatly affects infectious disease transmission pattern. We need more to study about under laying multifaceted casual affiliation between climate and communicable diseases and employ this information to the forecast of their upcoming impact. © 2020 The author (s).

Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature.

Coronavirus disease 2019 (COVID-19) has become a significant global public health issue resulting from SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). COVID-19 outbreak approaches an unprecedented challenge for human health, the economy, and societies. The transmission of the COVID-19 is influenced by many factors, including climatic, environmental, socioeconomic, and demographic. This study aimed to investigate the influences of climatic and sociodemographic determinants on COVID-19 transmission. The climatic variables considered herein were air temperature, relative humidity, wind speed, air pollution, and cumulative precipitation. Sociodemographic variables included population density, socioeconomic conditions, misinformation, and personal hygiene practices towards the pandemic. Review results indicated that lower temperatures and greater incidence of COVID-19 are reported in a more significant number of studies. Another factor linked to COVID-19 occurrence was the humidity. However, the results were varied; some research reported positive, and others reported negative relationships. In addition, poor air quality, along with strong winds, makes the virus more vulnerable to spreading, leading to a spike in COVID-19 cases. PM2.5, O3, and NO2 also showed a strong correlation with the recent epidemic. The findings on rainfall were inconsistent between studies. Among the non-climatic factors, population density, education, and income were credited as potential determinants for the coronavirus outbreak. Climatic and sociodemographic factors showed a significant correlation on the COVID-19 outbreak. Thus, our review emphasizes the critical importance of considering climatic and non-climatic factors while developing intervention measures. This study's core findings will support the decision-makers in identifying climatic and socioeconomic elements that influence the risks of future pandemics.

Climatic and Environmental Factors Influencing COVID-19 Transmission-An African Perspective.

Since the outbreak of COVID-19 was decreed by the World Health Organization as a public health emergency of worldwide concern, the epidemic has drawn attention from all around the world. The disease has since spread globally in developed and developing countries. The African continent has not been spared from the pandemic; however, the low number of cases in Africa compared to developed countries has brought about more questions than answers. Africa is known to have a poor healthcare system that cannot sustain the emerging infectious disease pandemic. This study explored climatic and environmental elements influencing COVID-19 transmission in Africa. This study involved manuscripts and data that evaluated and investigated the climatic and environmental elements of COVID-19 in African countries. Only articles written in English were considered in the systematic review. Seventeen articles and one database were selected for manuscript write-ups after the review process. The findings indicated that there is evidence that suggests the influence of climatic and environmental elements on the spread of COVID-19 in the continent of Africa; however, the evidence needs more investigation in all six regions of Africa and at the country level to understand the role of weather patterns and environmental aspects in the transmission of COVID-19.

Environmental factors and infectious diseases: a systematic review

Environmental factors and infectious diseases are closely related, mathematical models seek to explain these interactions, however, the same analytical practices are often observed with infectious diseases despite substantial differences from non-infectious diseases that can result in analytical challenges.

Seasonal Infectious Disease Prediction based on Electronic Patient Health Records using Boosted Random Forest Algorithms

Environmental condition and climatic factors are important elements of infection outbreaks. Some illness can change their effect of spread based on the weather condition. Such as Dengue increase their transmission in monsoon and winter seasons. Because these are rainy periods. So the wet occasions can provoke the dengue mosquito production. Meteorological changes play an essential aspect to trigger a new sickness. And also it produces new pathogens. Machine learning is an efficient component to predict seasonal wise infectious disease depends on climatic changes. In this paper, the researcher discusses three pandemics such as COVID-19, Dengue and Flu. As well as how these three epidemics can modify their dissemination based upon the weather pattern. Furthermore, the researcher predicts which season influences which ailment based on the patient health records. And also discuss various disease prediction algorithms such as Naive bayes, Decision tree, KNN (K-nearest neighbors), Boosted random forest and SVM (Support vector machine). The Boosted random Forest algorithm gives the 95% accuracy for forecasting the seasonal infections based on the patients electronic health record. By using Boosted Random Forest algorithm the researcher finds the winter season is the most suitable season produces high aliments compared to other seasons. It is very helpful to the health analyst to identify the seasonal diseases © 2022 IEEE.

SPATIAL MODELLING OF KEY REGIONAL-LEVEL FACTORS OF COVID-19 MORTALITY IN RUSSIA

Intensive socio-economic interactions are a prerequisite for the innovative development of the economy, but at the same time, they may lead to increased epidemiological risks. Persistent migration patterns, the socio-demographic composition of the population, income level, and employment structure by type of economic activity determine the intensity of socio-economic interactions and, therefore, the spread of COVID-19. We used the excess mortality (mortality from April 2020 to February 2021 compared to the five-year mean) as an indicator of deaths caused directly and indirectly by COVID-19. Similar to some other countries, due to irregularities and discrepancies in the reported infection rates, excess mortality is currently the only available and reliable indicator of the impact of the COVID-19 pandemic in Russia. We used the regional level data and fit regression models to identify the socio-economic factors that determined the impact of the pandemic. We used ordinary least squares as a baseline model and a selection of spatial models to account for spatial autocorrelation of dependent and independent variables as well as the error terms. Based on the comparison of AICc (corrected Akaike information criterion) and standard error values, it was found that SEM (spatial error model) is the best option with reliably significant coefficients. Our results show that the most critical factors that increase the excess mortality are the share of the elderly population and the employment structure represented by the share of employees in manufacturing (C economic activity according to European Skills, Competences, and Occupations (ESCO) v1 classification). High humidity as a proxy for temperature and a high number of retail locations per capita reduce the excess mortality. Except for the share of the elderly, most identified factors influence the opportunities and necessities of human interaction and the associated excess mortality. © 2022, Russian Geographical Society. All rights reserved.

A study of the potential relationship between COVID-19 (Corona virus) daily outbreak and temperature changes in Iran during March 2020

Climate is one of the most important structural factors on planet Earth and it is undoubtedly natural and all manifestations are at a wide range of levels. To recognize, control and adapt it, scientists have tried to improve life and well-being under various climatic conditions. Decreasing ambient temperatures can be common or annihilated by various diseases. The aim of this study is to investigate the possible relationship between covid-19 (Corona virus) disease and temperature fluctuations in Iran during March 2020. This is, of course, a preliminary study, and further research may explore other aspects of the subject. The methodology applied was the chi-square statistic to evaluate the test of independence between the studied variables and linear regression analysis to find the possibility of the influence of temperature as an independent climatic factor in the increase or decrease of the number of patients. In case of failure of linear regression analysis, the quadratic fit estimation was used. Results of this study proved the slight significant correlation between the occurrence of Corona and the temperature changes in the study area during March 2020. Although the temperature element is considered to be an influential climatic factor in Corona-like diseases such as influenza or SARS, this study could not profoundly prove such a relationship. The prevalence of Corona in Iran can be affected by several other factors such as deficiency of medical equipment, the behavior of the Iranian people towards the epidemic and the spread of this virus, socio-political and cultural gatherings.

Is nano-biofortification the right approach for malnutrition in the era of COVID-19 and climate change?

Human health may depend on the environment and its compartments, which may include climatic factors. These climatic factors and their changes might impact on human health particularly the outbreak of pandemics like COVID-19. The combined stress resulted from climate changes and COVID-19 could be noticed in several countries especially in the developing countries. Malnutrition is considered one of the most important problems in the developing countries in particular under the droughts, flooding, and other climatic events. Malnutrition was aggravated under COVID-19 outbreak in these countries due to the closure of borders between countries, the crisis of global trade, and the global food insecurity. The biofortification process is the sustainable solution to overcome malnutrition, which included very recently using nano-nutrients as called nano-biofortification. The approach of nano-biofortification is a promising tool in producing biofortified edible plants, otherwise this tool still needs more studies to answer the open questions like which nano-nutrients can be used in nano-biofortification? Which recommended doses and crops are considered suitable candidates?

Quality assessment of honey in three different geographical areas from Republic of Moldova

One of the empirical and image products of the Republic of Moldova is bee honey. Its assortment is due to the variety of landforms, as well as the diversity of flora specific to geographical regions. During the Covid-19 pandemic, domestic consumers had limited access to bee honey. This was caused by the restrictions imposed in order to organize fairs and agricultural markets which are one of the main sources for the purchase of bee products in the Republic of Moldova. At the same time, the analysis of trademarks in supermarkets highlighted the preferences of honey consumption as follows: polyfloral honey-28.57%, lime honey-20.40% and acacia honey-14.28%. In order to evaluate the quality of honey from small producers and highlight the specific characteristics of geographical areas, 60 samples were analyzed that included 3 types of honey: polyflora, sunflower and linden collected from 3 different geographic areas: Soroca area (North), area Ungheni (Center), Stefan Voda area (South). Honey samples were declared harvest of 2020. They were analyzed physico-chemically using methods provided by national and EU standards. The results obtained from the analysis of pollen in honey confirm the botanical origin declared by beekeepers and allowed to highlight the types of pollen specific to each area. Following the determination of qualitative indices: reaction with ethyl alcohol, with resorcinol;insoluble matter, cereal flour, gelatin and starch, it was found that the most inconsistencies were found in linden honey. Some indicators were close to the maximum permissible values specified in regulatory documents. The water content found in the honey samples was quite varied, and ranged from 16.05% +or- 0.11% to 19.89% +or- 0.13%, but these samples were within the limits of the standards. Total acidity ranged from 6.19 +or- 0.13 to 27.20 +or- 0.03 which falls within the established norms (up to 50.00 cm3 NaOH solution in (milliequivalents) per 100 g of honey). According to the SIE Lab space, all samples have a yellowish tint, this is indicated by positive values on the b* axis. However, honey samples from the southern region of Moldova showed the presence of greenish pollen, as evidenced by the reduced values on the a* axis. Our research reveals the quality level of honey from three different areas of the Republic of Moldova and the variation of quality parameters due to factors such as geographical region, climatic conditions, botanical origin and handling or storage conditions.

Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19.

Recently, the whole world witnessed the fatal outbreak of COVID-19 epidemic originating at Wuhan, Hubei province, China, during a mass gathering in a film festival. World Health Organization (WHO) has declared this COVID-19 as a pandemic due to its rapid spread across different countries within a few days. Several research works are being performed to understand the various influential factors responsible for spreading COVID. However, limited studies have been performed on how climatic and socio-demographic conditions may impact the spread of the virus. In this work, we aim to find the relationship of socio-demographic conditions, such as temperature, humidity, and population density of the regions, with the spread of COVID-19. The COVID data for different countries along with the social data are collected. For the experimental purpose, Fuzzy association rule mining is employed to infer the various relationships from the data. Moreover, to examine the seasonal effect, a streaming setting is also considered. The experimental results demonstrate various interesting insights to understand the impact of different factors on spreading COVID-19.

How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India.

Global temperature rises in response to accumulating greenhouse gases is a well-debated issue in the present time. Historical records show that greenhouse gases positively influence temperature. Lockdown incident has brought an opportunity to justify the relation between greenhouse gas centric air pollutants and climatic variables considering a concise period. The present work has intended to explore the trend of air quality parameters, and air quality induced risk state since pre to during the lockdown period in reference to India and justifies the influence of pollutant parameters on climatic variables. Results showed that after implementation of lockdown, about 70% area experienced air quality improvement during the lockdown. The hazardous area was reduced from 7.52% to 5.17%. The spatial association between air quality components and climatic variables were not found very strong in all the cases. Still, statistically, a significant relation was observed in the case of surface pressure and moisture. From this, it can be stated that pollutant components can control the climatic components. This study recommends that pollution source management could be a partially good step for bringing climatic resilience of a region.

Socio-economic factors do also matter: comments on the article "Can climatic factors explain the differences in COVID-19 incidence and severity across the spanish regions?: an ecological study".

A report published in this journal showed an inversely significant association between ultraviolet radiation (UVR) before the pandemic and cumulative COVID-19 cases in Spain. The analyses employed several meteorological factors, but socio-economic factors were not included. We examined the associations of COVID-19 cases with selected factors and found a significance on gross domestic product per capita (p = 0.037 by Spearman's correlation). Hence, simple regression analyses of UVR would be confounded with regional difference in economic activities. In addition, we raised several questions for limitations due to the study design and analyses.

Climate effects on the COVID-19 outbreak: a comparative analysis between the UAE and Switzerland.

The main aim of the present study is to disclose the similarities or differences of the climate effects on the COVID-19 outbreak in two countries, which have different climatic conditions. Using the correlation modeling, the results revealed that some climatic factors, such as the ULR, temperature, and CH4 in the UAE and aerosol index and NO2 in Switzerland have positive lagged correlations with the outburst of COVID-19 by intensifying role within - 9, - 7, and - 2 days. The mitigating role was also observed for ozone/solar radiation and temperature/long-wave radiation in the UAE and Switzerland, respectively. The initial hypotheses of the research have confirmed the correlations between new cases of COVID-19 and ULR and aerosol indices in the UAE and Switzerland. However, the main finding revealed that the climate effects on the COVID-19 outbreak show different roles in the different countries, locating in dissimilar climatic zones. Accordingly, the COVID-19 can be intensified by increases of the ULR and temperature in an arid region, while it can be exactly mitigated by increases of these factors in a temperate area. This finding may be useful for future researches for identifying the essential influencing factors for the mitigating COVID-19 outbreak.

Genetic Diversity of SARS-CoV2 and Environmental Settings: Possible Association with Neurological Disorders.

The new coronavirus (CoV), called novel coronavirus disease 2019 (COVID-19), belongs to the Coronaviridae family which was originated from the sea market in Wuhan city in China, at the end of the year 2019. COVID-19 and severe acute respiratory syndrome (SARS) are belonging to the same family (Coronaviridae). The current outbreak of COVID-19 creates public concern and threats all over the world and now it spreads out to more than 250 countries and territories. The researchers and scientists from all over the world are trying to find out the therapeutic strategies to abate the morbidity and mortality rate of the COVID-19 pandemic. The replication, spreading, and severity of SARS-CoV2 depend on environmental settings. Noteworthy, meteorological parameters are considered as crucial factors that affect respiratory infectious disorders, although the controversial effect of the meteorological parameter is exposed against COVID-19. Besides, COVID-19 accelerates the pathogenesis of the neurological disorders. However, the pathogenic mechanisms between COVID-19 and neurological disorders are still unclear. Hence, this review is focused on the genomics and ecology of SARS-CoV2 and elucidated the effects of climatic factors on the progression of COVID-19. This review also critically finds out the vulnerability between COVID-19 and neurological disorders based on the latest research data.

COVID-19 and climatic factors: A global analysis.

BACKGROUND: It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce. OBJECTIVE: To examine the association between climatic factors and COVID-19. METHODS: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI). RESULTS: Data from 206 countries/regions (until April 20, 2020) with ≥100 reported cases showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity of <5 g/m3, an absolute humidity of 5-10 g/m3 was associated with a 23% (95% CI: 6-42%) higher rate of COVID-19 cases, while absolute humidity >10 g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis. CONCLUSION: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5-10 g/m3 may be suggestive of a 'sweet point' for viral transmission, however only controlled laboratory experiments can decisively prove it.

Can climatic factors explain the differences in COVID-19 incidence and severity across the Spanish regions?: An ecological study.

BACKGROUND: Environmental factors play a central role in seasonal epidemics. SARS-CoV-2 infection in Spain has shown a heterogeneous geographical pattern This study aimed to assess the influence of several climatic factors on the infectivity of SARS-CoV-2 and the severity of COVID-19 among the Spanish Autonomous Communities (AA.CC.). METHODS: Data on coronavirus infectivity and severity of COVID-19 disease, as well as the climatic variables were obtained from official sources (Ministry of Health and Spanish Meteorological Agency, respectively). To assess the possible influence of climate on the development of the disease, data on ultraviolet radiation (UVR) were collected during the months before the start of the pandemic. To analyze its influence on the infectivity of SARS-CoV-2, data on UVR, temperature, and humidity were obtained from the months of highest contagiousness to the peak of the pandemic. RESULTS: From October 2019 to January 2020, mean UVR was significantly related not only to SARS-CoV-2 infection (cumulative incidence -previous 14 days- × 105 habitants, rho = - 0.0,666; p = 0.009), but also with COVID-19 severity, assessed as hospital admissions (rho = - 0.626; p = 0.017) and ICU admissions (rho = - 0.565; p = 0.035). Besides, temperature (February: rho = - 0.832; p < 0.001 and March: rho = - 0.904; p < 0.001), was the main climatic factor responsible for the infectivity of the coronavirus and directly contributed to a different spread of SARS-CoV-2 across the Spanish regions. CONCLUSIONS: Climatic factors may partially explain the differences in COVID-19 incidence and severity across the different Spanish regions. The knowledge of these factors could help to develop preventive and public health actions against upcoming outbreaks of the disease.

[The Gastein Healing Gallery and a Potential Risk of Viral Infections in the Treatment Area]. / Der Gasteiner Heilstollen und eine mögliche Ansteckungsgefahr im Therapiebereich mit Viren.

The SARS-CoV­2 has infected millions of humans worldwide in the past few months and hundreds of thousands have died as a result of an infection. The end of the pandemic is not in sight and many people are anxious of becoming infected in different settings. The Gastein Healing Gallery (GHG) is a unique outpatient facility combining heat, high humidity and mild radon radiation. Every year approximately 12,000 patients with inflammatory rheumatic, degenerative diseases and chronic pain are treated. We have therefore reviewed and analyzed the literature with respect to a possible increased risk of infection for patients during treatment in the GHG. On the one hand the climatic and physical conditions in the GHG can be viewed as hostile to viruses and on the other hand the mild radon hyperthermia and the geographic location of the GHG lead to positive effects on the patient's health via complex physiological processes. We therefore consider the likelihood of infection with viruses in the GHG in no way increased, in contrast, it is probably considerably lower compared to other settings.

Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran.

The COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1st-level Response to Major Public Health Emergency in each region, peaking after 1-2 days, reaching their inflection points after 10-19 days, and dropping to zero after 11-18 days since the 1st-level response was launched. By March 29th, the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25th, in Italy as in Guangdong on February 25th, and in Iran as in Xinjiang on March 8th. The results showed that by June 15th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide.