Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns

The correspondence between the physiological cold tolerance and the distribution border of Mediterranean sandflies in Southeast Europe

BACKGROUND OBJECTIVES

The distribution of sandflies is influenced by several climatic factors, like winter temperature limits, summer humidity, and drought. Only a few studies were performed to search for the connection between the physiological tolerance of sandflies and the result of the Climate Envelope Models projected distribution borders.

METHODS

It was studied how the depth of rodent burrows could determine the occurrence of five European Phlebotomus species based on their known physiological cold tolerances.

RESULTS

The modelled distributions based on the monthly minimum temperatures showed notable differences by soil depth and species. The observed and modelled European distribution areas of sandflies according to - 20 cm soil depth showed good coincidence with the observed occurrences of Phlebotomus neglectus , Ph. papatasi , and Ph. perfiliewi in East-Central, Southeast, and East Europe. Also, a good coincidence was found in the case of the modelled and observed occurrences of Phlebotomus perniciosus in Western Europe.

INTERPRETATION CONCLUSION

It can be concluded that in their north-eastern distribution borders, sandflies can overwinter in rabbit and rodent burrows at depths of - 20 cm below the surface. The physical cold tolerance of Ph. ariasi could be underestimated. In more than - 50 cm of depth, the stable and continuously cold environment of the shelters (mean temperature less than 10°C) could inhibit the post-winter diapause activation of sandflies in the northern regions.

Seasonality of endemic COVID-19

IMPORTANCE

The seasonality of COVID-19 is important for effective healthcare and public health decision-making. Previous waves of SARS-CoV-2 infections have indicated that the virus will likely persist as an endemic pathogen with distinct surges. However, the timing and patterns of potentially seasonal surges remain uncertain, rendering effective public health policies uninformed and in danger of poorly anticipating opportunities for intervention, such as well-timed booster vaccination drives. Applying an evolutionary approach to long-term data on closely related circulating coronaviruses, our research provides projections of seasonal surges that should be expected at major temperate population centers. These projections enable local public health efforts that are tailored to expected surges at specific locales or regions. This knowledge is crucial for enhancing medical preparedness and facilitating the implementation of targeted public health interventions.

Significance of wastewater surveillance in detecting the prevalence of SARS-CoV-2 variants and other respiratory viruses in the community - A multi-site evaluation

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome in wastewater has proven to be useful for tracking the trends of virus prevalence within the community. The surveillance also provides precise and early detection of any new and circulating variants, which aids in response to viral outbreaks. Site-specific monitoring of SARS-CoV-2 variants provides valuable information on the prevalence of new or emerging variants in the community. We sequenced the genomic RNA of viruses present in the wastewater samples and analyzed for the prevalence of SARS-CoV-2 variants as well as other respiratory viruses for a period of one year to account for seasonal variations. The samples were collected from the Reno-Sparks metropolitan area on a weekly basis between November 2021 to November 2022. Samples were analyzed to detect the levels of SARS-CoV-2 genomic copies and variants identification. This study confirmed that wastewater monitoring of SARS-CoV-2 variants can be used for community surveillance and early detection of circulating variants and supports wastewater-based epidemiology (WBE) as a complement to clinical respiratory virus testing as a healthcare response effort. Our study showed the persistence of the SARS-CoV-2 virus throughout the year compared to a seasonal presence of other respiratory viruses, implicating SARS-CoV-2's broad genetic diversity and strength to persist and infect susceptible hosts. Through secondary analysis, we further identified antimicrobial resistance (AMR) genes in the same wastewater samples and found WBE to be a feasible tool for community AMR detection and monitoring.

Seasonality, mass vaccination and critical policy evaluation on global exit strategy of COVID-19 crisis

There is a strong coordinated effort by vaccination groups all over the world to put an end to the current crisis of COVID-19. Now sufficient data are available to analyse and compare some results to explore the aftereffects of vaccination. Some influence variables on transmissions of the disease were discussed e.g., mass vaccination, lockdown and seasonality. Most studies covered here are up to the beginning of July 2022, while some analyses focused on the earlier period of mass vaccination. Well established, simple statistical techniques to evaluate results were presented those used open data sources of authoritative bodies. Some comparisons between vaccinated vs. unvaccinated were also discussed based on data from UK Government Health Security Agency (UHSA). In terms of mass vaccination, adverse reactions after vaccination received attention, as health and safety issues of the general public are of prime importance. Apart from direct side effects, the secondary effect of mass vaccination needs attention too. After the initiation of the vaccination programme, almost all countries experienced a sudden surge in transmission and most countries had to impose strict lockdown measures. Many countries, with a low prevalence of disease, suddenly showed a steep jump and some countries even followed a synchronized pattern between the rate of transmissions and the variation of vaccine doses. Time series analyses and bar diagram presentations were able to capture those features. In that context, fast mutation of the virus and new variants after mass vaccination and possible mechanisms/consequences were also attended. To understand the effect of seasonality, similarities between COVID-19 and the seasonal Flu are discussed for Europe and US to gain useful insight. Using time series analyses and spatial plots of regional temperature composites we showed, like Flu, seasonality played a dominant role in transmissions of COVID-19 in the Europe. Regulations of vaccine dose and policy implication were explored too. From 22^nd December 2021, global vaccine doses were reduced substantially, which followed a dramatic reduction in cases and thereafter deaths with around one month's lag between each. As strong dependency on seasonality is noticed in certain countries and observing that regulation of vaccine doses has roles in modulating the transmission with certain lags, globally as well as regionally, our results have policy implications for the management of COVID. Debating, questioning and criticism are always the foundation of great science and the major pillars of its progress. Following that objective, it is an effort to explore pragmatically, supported by scientific analyses, areas relating to the effectiveness of the COVID-19 vaccine and the exit strategy via the pathway of vaccination.

Early-phase pandemic in Italy: Covid-19 spread determinant factors

Although the Covid-19 pandemic is still ongoing, the environmental factors beyond virus transmission are only partially known. This statistical study has the aim to identify the key factors that have affected the virus spread during the early phase of pandemic in Italy, among a wide set of potential determinants concerning demographics, environmental pollution and climate. Because of its heterogeneity in pollution levels and climate conditions, Italy provides an ideal scenario for an ecological study. Moreover, the selected period excludes important confounding factors, as different virus variants, restriction policies or vaccines. The short-term relationship between the infection maximum increase and demographic, pollution and meteo-climatic parameters was investigated, including both winter-spring and summer 2020 data, also focusing separately on the two seasonal periods and on North vs Centre-South. Among main results, the importance of population size confirmed social distancing as a key management option. The pollution hazardous role undoubtedly emerged, as NO2 affected infection increase in all the studied scenarios, PM2.5 manifested its impact in North of Italy, while O3 always showed a protective action. Whereas higher temperatures were beneficial, especially in the cold season with also wind and relative humidity, solar irradiance was always relevant, revealing several significant interactions with other co-factors. Presented findings address the importance of the environment in Sars-CoV-2 spread and indicated that special carefulness should be taken in crowded areas, especially if they are highly polluted and weakly exposed to sun. The results suggest that containment of future epidemics similar to Covid-19 could be supported by reducing environmental pollution, achieving safer social habits and promoting preventive health care for better immune system response, as an only comprehensive strategy.

Combating COVID-19 crisis and exploring heat-based simple solutions

Covid-19 pandemic affected whole of the world taking many lives and impacting the economy and mental health severely. Exit pathways via vaccination though ignited optimism initially but attenuated by the emergence of several new variants which are less sensitive to vaccines. Considering emergency situations, some urgent, simple heat-based solutions for the initial stages of the disease were also proposed at the beginning of pandemic and further elaborated here. Solutions were proposed based on science as follows: exploring results of statistical analyses on the global transmission of COVID-19; observed temperature-dependent behaviours of similar category viruses; temperature-based clinical trial experiments with similar category viruses; successful clinical trial experiments with heat-based intervention for COVID-19 patients; and finally, biological mechanism/response in human bodies to heat-based solution for COVID-19 from medical doctor's perspective. Solutions proposed are practically without side effects, can be even practised in own home and there is no vested interest involved.

Lessons to be Learned: The Knowledge, Attitudes, and Practices of Turkish People towards the COVID-19 Pandemic

Background

The world experienced the greatest pandemic of the 21^st century with the emergence of a new and readily transmissible the coronavirus disease. Understanding knowledge, attitudes, and practices (KAP) of the public towards the pandemic is an essential part of developing effective preventive strategies.

Aim

The objective of this study was to investigate the knowledge, attitudes, and practices (KAP) concerning the coronavirus (COVID-19) among population in Istanbul.

Methods

This is a cross-sectional and multi-stage, stratified random sampling based on multi-center population of Istanbul. A total of 5,414 persons were contacted and 4361 participants (80.5%) gave consent. The data were analyzed using descriptive and multiple regression analyses.

Results

There were significant differences between low education and high educational level with respect to age groups, gender, occupation, income, residence, number of rooms and family members (P < 0.001). Responses concerning knowledge of COVID-19 indicated that subjects with high education level were significantly higher regarding knowledge of the signs and symptoms of COVID-19 and methods of detecting COVID-19 respectively. Majority of the participants consider COVID-19 risk is higher than AIDS or Cancer (75.8% of low education vs. 67.2% of high education level (P < 0.001). Multivariate stepwise regression analysis revealed that monthly income status (P < 0.001), appropriate method of detecting COVID-19 (P < 0.001), occupational status (P < 0.001), medical mask prevent against COVID-19 (P < 0.001), eating or contacting wild animals (P < 0.001), isolation and treatment of people reduce risk (P < 0.001), isolation 14 days (P < 0.001), avoid going to crowded places such as train-metro, bus, restaurants and shopping (P = 0.003), COVID-19 spreads via-respiratory droplets (P = 0.004), afraid of travel (P = 0.026) were significantly associated with COVID-19 knowledge.

Conclusions

The current study results revealed that the educational level and occupation especially sedentary are correlated positively with knowledge, attitude and practices. This finding is not surprising since higher education levels and professional status are associated with good KAP in most epidemic diseases including COVID-19. Nevertheless, the recent experience with COVID-19 has provided lessons on strategy and policy making.

SARS-CoV-2 Viral Load Analysis at Low and High Altitude: A Case Study from Ecuador

SARS-CoV-2 has spread throughout the world, including remote areas such as those located at high altitudes. There is a debate about the role of hypobaric hypoxia on viral transmission and COVID-19 incidence. A descriptive cross-sectional analysis of SARS-CoV-2 infection and viral load among patients living at low (230 m) and high altitude (3800 m) in Ecuador was completed. Within these two communities, the total number of infected people at the time of the study was 108 cases (40.3%). The COVID-19 incidence proportion at low altitude was 64% while at high altitude was 30.3%. The mean viral load from those patients who tested positive was 3,499,184 copies/mL (SD = 23,931,479 copies/mL). At low altitude (Limoncocha), the average viral load was 140,223.8 copies/mL (SD = 990,840.9 copies/mL), while for the high altitude group (Oyacachi), the mean viral load was 6,394,789 copies/mL (SD = 32,493,469 copies/mL). We found no statistically significant differences when both results were compared (p = 0.056). We found no significant differences across people living at low or high altitude; however, men and younger populations had higher viral load than women older populations, respectively.

Clinical informatics solutions in COVID-19 pandemic: Scoping literature review

Background

The global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.

Methods

A comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.

Results

In this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).

Conclusion

The present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

Autopsy results from a COVID-19 patient treated in a tropical area, and a review of the epidemiological history

Since the start of the COVID-19 pandemic, there has been an urgent need to produce accurate and sensitive tests. However, there have been instances where a positive nucleic acid test turns negative after treatment, and then positive again. This case report describes such an instance from the tropical region of Hainan, China. The patient was a 61-year-old female who went to Hainan on vacation from Wuhan during the COVID-19 pandemic in 2020. Symptoms appeared 9 d after arriving in Hainan, and it was confirmed that the nucleic acid test was positive after 4 repeats. Her condition declined rapidly, her heart stopped beating, and she was admitted in a coma to the ICU. After treatment, the SARS-CoV-2 virus nucleic acid test of several nasopharyngeal swabs were negative, and tests on whole blood, anal swabs, and urine were also negative. Later, however, nucleic acid tests on a lower respiratory tract sputum swab and lower respiratory tract lavage fluid were positive. An autopsy examination was carried out 12 h after her death, and multi-organ secretions were extracted for nucleic acid testing. The SARS-CoV-2 virus nucleic acid was only detected in the swabs from the end of the bronchus, which was confirmed by the visualization of the coronavirus by electron microscopy. Autopsy confirmed that the damage was mainly concentrated in the lungs and immune organs and tissues throughout the body. Epidemiology indicated that none of the people she came into contact with after arriving in Hainan, including close contacts, were infected. This is in sharp contrast to the highly contagious virus in Wuhan in the temperate zone during the same period. This case report indicates: (1) The high temperatures in tropical areas may have an impact on the spread and harm of COVID-19, and (2) The reason why nucleic acid testing for COVID-19 was initially negative and then positive after treatment may be related to the survival of the SARS-CoV-2 virus in deep lung tissues.

Novel Evidence Showing the Possible Effect of Environmental Variables on COVID-19 Spread

Coronavirus disease (COVID-19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID-19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID-19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID-19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID-19 cases/deaths per day generally showed a decreasing trend; the 2020-year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID-19 may increase as the latitude decreases.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate

The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.

A novel analysis approach to determining the case fatality rate of COVID-19 in Italy

BACKGROUND

The Coronavirus Disease 2019 (COVID-19), which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate.

OBJECTIVE

The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) to make more accurate forecasts of epidemic trends the of COVID-19 in Italy.

METHODS

We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of daily deaths and total deaths to calculate the discharged case fatality rate (tDCFR), monthly discharged case fatality rate (mDCFR), and stage discharged case fatality rate (sDCFR). Furthermore, we analyzed the trend in mDCFR between January and December 2020 using Joinpoint Regression Analysis and used the ArcGIS version 10.7 software to visualize the spatial distribution of epidemic case fatality rate and assigned different colors to each province based on the CFR or tDCFR.

RESULTS

We calculate the number and obtain the new index tDCFR and mDCFR for calculating the fatality rate. The results show that the overall trend of tDCFR and mDCFR fluctuates greatly from January to May. After reaching the peak, it first rises rapidly, then falls rapidly, and finally stabilizes. The map shows that the provinces with high tDCFR were Emilia-Romagna, Puglia and Lombardia. The change trend of mDCFR over time was divided into two stages, the first stage (from January to May) and the second stage (from June to December). Among the six selected countries, the United States has the highest tDCFR (4.26%), while the tDCFR of the remaining countries is between 0.98% and 2.72%.

CONCLUSIONS

We provide a new perspective for assessing the mortality of COVID-19 in Italy,which can use these ever-changing data to calculate a more accurate case fatality rate and scientifically predict the development trend of the epidemic.

CLINICALTRIAL

COVID-19: A Redox Disease-What a Stress Pandemic Can Teach Us About Resilience and What We May Learn from the Reactive Species Interactome About Its Treatment

Significance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19), affects every aspect of human life by challenging bodily, socioeconomic, and political systems at unprecedented levels. As vaccines become available, their distribution, safety, and efficacy against emerging variants remain uncertain, and specific treatments are lacking. Recent Advances: Initially affecting the lungs, COVID-19 is a complex multisystems disease that disturbs the whole-body redox balance and can be long-lasting (Long-COVID). Numerous risk factors have been identified, but the reasons for variations in susceptibility to infection, disease severity, and outcome are poorly understood. The reactive species interactome (RSI) was recently introduced as a framework to conceptualize how cells and whole organisms sense, integrate, and accommodate stress. Critical Issues: We here consider COVID-19 as a redox disease, offering a holistic perspective of its effects on the human body, considering the vulnerability of complex interconnected systems with multiorgan/multilevel interdependencies. Host/viral glycan interactions underpin SARS-CoV-2's extraordinary efficiency in gaining cellular access, crossing the epithelial/endothelial barrier to spread along the vascular/lymphatic endothelium, and evading antiviral/antioxidant defences. An inflammation-driven "oxidative storm" alters the redox landscape, eliciting epithelial, endothelial, mitochondrial, metabolic, and immune dysfunction, and coagulopathy. Concomitantly reduced nitric oxide availability renders the sulfur-based redox circuitry vulnerable to oxidation, with eventual catastrophic failure in redox communication/regulation. Host nutrient limitations are crucial determinants of resilience at the individual and population level. Future Directions: While inflicting considerable damage to health and well-being, COVID-19 may provide the ultimate testing ground to improve the diagnosis and treatment of redox-related stress diseases. "Redox phenotyping" of patients to characterize whole-body RSI status as the disease progresses may inform new therapeutic approaches to regain redox balance, reduce mortality in COVID-19 and other redox diseases, and provide opportunities to tackle Long-COVID. Antioxid. Redox Signal. 35, 1226-1268.

Coronavirus seasonality, respiratory infections and weather

Background

The survival of coronaviruses are influenced by weather conditions and seasonal coronaviruses are more common in winter months. We examine the seasonality of respiratory infections in England and Wales and the associations between weather parameters and seasonal coronavirus cases.

Methods

Respiratory virus disease data for England and Wales between 1989 and 2019 was extracted from the Second-Generation Surveillance System (SGSS) database used for routine surveillance. Seasonal coronaviruses from 2012 to 2019 were compared to daily average weather parameters for the period before the patient’s specimen date with a range of lag periods.

Results

The seasonal distribution of 985,524 viral infections in England and Wales (1989–2019) showed coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2 to 8. Ninety percent of infections occurred where the daily mean ambient temperatures were below 10 °C; where daily average global radiation exceeded 500 kJ/m²/h; where sunshine was less than 5 h per day; or where relative humidity was above 80%. Coronavirus infections were significantly more common where daily average global radiation was under 300 kJ/m²/h (OR 4.3; CI 3.9–4.6; p < 0.001); where average relative humidity was over 84% (OR 1.9; CI 3.9–4.6; p < 0.001); where average air temperature was below 10 °C (OR 6.7; CI 6.1–7.3; p < 0.001) or where sunshine was below 4 h (OR 2.4; CI 2.2–2.6; p < 0.001) when compared to the distribution of weather values for the same time period. Seasonal coronavirus infections in children under 3 years old were more frequent at the start of an annual epidemic than at the end, suggesting that the size of the susceptible child population may be important in the annual cycle.

Conclusions

The dynamics of seasonal coronaviruses reflect immunological, weather, social and travel drivers of infection. Evidence from studies on different coronaviruses suggest that low temperature and low radiation/sunlight favour survival. This implies a seasonal increase in SARS-CoV-2 may occur in the UK and countries with a similar climate as a result of an increase in the R₀ associated with reduced temperatures and solar radiation. Increased measures to reduce transmission will need to be introduced in winter months for COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06785-2.

The association between initial COVID-19 spread and meteorological factors in Indonesia

On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and a half months later (15/05/2020), the cumulative number of infection cases was 16,496, with a total of 1076 mortalities. This study investigates the possible role of weather in the early cases of COVID-19 in six selected cities in Indonesia. Daily temperature and relative humidity data from weather stations nearby in each city were collected from March 3 to April 30, 2020, corresponding with COVID-19 incidence. Correlation tests and regression analysis were performed to examine the association of those two data series. Moreover, we analyzed the distribution of COVID-19 referring the weather data to estimate the effective range of weather data supporting the COVID-19 incidence. Our result reveals that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) present significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak, with the strongest correlations found at the 5-day lag, i.e., 0.37 (- 0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that most COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31 °C and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a more prominent role and should be given greater consideration in preventing the spread of COVID-19.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42398-021-00202-9.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19

The role of meteorological and air quality factors in moderating the transmission of SARS-CoV-2 and severity of COVID-19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID-19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID-19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID-19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Association of temperature and relative humidity with the growth rate of the coronavirus disease 2019 epidemic

The effects of temperature and relative humidity on the growth of coronavirus disease 2019 (COVID-19) remain unclear. Data on the COVID-19 epidemic that were analyzed in this study were obtained from the official websites of the National Health Commission of China and the Health Commissions of 31 provinces in China. From January 26 to February 25, 2020, the cumulative number of confirmed COVID-19 cases in each region was counted daily using data from our database. Curve fitting of daily scatter plots of the relationship between epidemic growth rate (GR) with average temperature (AT) and average relative humidity (ARH) was conducted using the loess method. The heterogeneity across days and provinces was calculated to assess the necessity of using a longitudinal model. Fixed-effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. An increased AT markedly reduced the GR when the AT was lower than -5°C, the GR was moderately reduced when the AT ranged from -5°C to 15°C, and the GR increased when the AT exceeded 15°C. ARH increased the GR when it was less than 72% and reduced the GR when it exceeded 72%. The temperature and relative humidity curves were not linearly associated with the GR of COVID-19. The GR was moderately reduced when the AT ranged from -5°C to 15°C. When the AT was lower or higher than -5°C to 15°C, the GR of COVID-19 increased. An increased ARH increased the GR when the ARH was lower than 72% and reduced the GR when the ARH exceeded 72%.

Weather and COVID-19 Deaths During the Stay-at-Home Order in the United States

OBJECTIVE

To estimate the association between weather and COVID-19 fatality rates during US stay-at-home orders.

METHODS

With a county-level longitudinal design, this study analyzed COVID-19 deaths from public health departments' daily reports and considered exposure as the 18 to 22 day-period before death. Models included state-level social distancing measures, Census Bureau demographics, daily weather information, and daily air pollution. The primary measures included minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V. light index.

RESULTS

A 1 °F increase in the minimum temperature was associated with 1.9% (95% CI, 0.2% to 3.6%) increase in deaths 20 days later. An ozone concentration increase of 1 ppb (part per billion) decreased daily deaths by 2.0% (95% CI, 0.1% to 3.6%); ozone levels below 38 ppb negatively correlated with deaths.

CONCLUSIONS

Increased mobility may drive the observed association of minimum daily temperature on COVID-19 deaths.

Does blood type affect the COVID-19 infection pattern?

Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i) the great geographical differences in the degree of virus contagiousness and lethality that were found in the different phases of the epidemic progression, and, ii) the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease. A recent hypothesis could shed some light on both aspects. Specifically, it has been proposed that, in the subject-to-subject transfer, SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject. Thus these conserved antigens can potentially cause an immune reaction in a receiving subject that has previously acquired specific antibodies for the source subject antigens. This hypothesis implies a blood type-dependent infection rate. The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of the observed heterogeneity in the epidemics spread. Here, we present an epidemiological deterministic model where the infection rules based on blood types are taken into account, and we compare our model outcomes with the exiting worldwide infection progression data. We found an overall good agreement, which strengthens the hypothesis that blood types do play a role in the COVID-19 infection.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

Coronavirus disease (COVID-19) has infected more than 50 million people and killed more than one million, worldwide, during less than a year course. COVID-19, which has already become the worst pandemic in the last 100 years, is still spreading worldwide. Since the beginning of the outbreak, it has been of particular interest to understand whether COVID-19 is seasonal; the finding might help for better planning and preparation for the fight against the disease. Over the past 12 months, numerous empirical and epidemiological studies have been performed to define the distinct diffusion patterns of COVID-19. Thereby, a wealth of data has accumulated on the relationship between various seasonal meteorological factors and COVID-19 transmissibility at global and local scales. In this review, we aimed to discuss whether COVID-19 exhibits any seasonal features in a global and local perspective by collecting and providing summaries of the findings from empirical and epidemiological studies on the COVID-19 pandemic during its first seasonal cycle.

Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

On the usefulness of the bioclimatic correlative models of SARS-CoV-2

This paper addresses the effects of atmospheric conditions on the spread of the SARS-CoV-2 coronavirus and its associated disease, COVID-19. For this purpose, we assess the limitations of bioclimatic correlative models to explain the geographic distribution of SARS-CoV-2 in the context of medical geography. Overall, there is a broad consensus that the global distribution of COVID-19 is not random but conditioned by environmental drivers. However, as the COVID-19 distribution becomes global, including tropical climates, the evidence reveals that atmospheric conditions explain, at most, only a limited amount of the space-time dynamics of SARS-CoV-2. Therefore, the usefulness of approaches based on bioclimatic envelopes is in question since the dominant route for the spread of COVID-19 seems to be the anthroposphere's non-stationary environment. In this sense, there is a need to clarify further the role of different transmission routes at multiple scales and outdoor and indoor environments beyond bioclimatic envelopes. At this time, the possible influence of the weather in COVID-19 spread is not sufficient to be taken into account in public health policies. Hence, until reliable bioclimatic envelopes of SARS-CoV-2, if any, are found, caution should be exercised when reporting, as this could have unforeseen consequences.

COVID-19 infection in Hokkaido, Japan might depend on the viscosity of atmospheric air

BACKGROUND

The large number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has plunged the world into fear in recent times. In Japan, 18,769 novel coronavirus disease 2019 (COVID-19) cases have been reported as of June 30, 2020. This study aimed to assess whether cluster infection prevention is possible by evaluating the association between viral transmission and meteorological factors.

METHODS

This study included 1263 people who were successively diagnosed with COVID-19 in Hokkaido, Japan between January 24, 2020 and June 30, 2020. After obtaining the values from the Japanese Meteorological Agency, the average scores of air temperature and humidity were calculated and compared with COVID-19 reproduction numbers, and the association between COVID-19 incidence or reproduction number and meteorological factors was assessed.

RESULTS

The COVID-19 reproduction number in Hokkaido had three peaks that came several days before the surge in COVID-19 cases. The peaks are indicative of cluster infections. There was a strong negative correlation between the kinematic viscosity of atmospheric air and the reproduction number.

DISCUSSION AND CONCLUSION

Analysis of the reproduction number is important for predicting or suppressing COVID-19 infection clusters. The authors found a strong association between meteorological factors, such as kinematic viscosity of atmospheric air and the incidence of COVID-19 infection. Meteorological forecasts could provide foreknowledge about COVID-19 infection clusters in the future.

Drivers of Infectious Disease Seasonality: Potential Implications for COVID-19

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system.

Diversity of SARS-CoV-2 isolates driven by pressure and health index

One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate (mr) is observed to be maximum at a particular pressure, beyond which mr maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure (Pm), whereas, for P > Pm and P < Pm, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

Coronavirus disease 2019 and asthma, allergic rhinitis: molecular mechanisms and host-environmental interactions

PURPOSE OF REVIEW

Coronavirus disease 2019 (COVID-19), a respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), is a pandemic in over 120 countries worldwide. Risk factors for severe COVID-19 include older age, ethnicity, sex, comorbidities, and living conditions. Although asthmatics and those with allergies are susceptible to more severe outcomes to viral infections, interestingly, asthma has not been reported to be a major comorbidity of COVID-19. However, there are some conflicting reports on the impact of asthma on COVID-19. The underlying immunological and molecular mechanisms may explain at least in part these observations. Furthermore, environmental factors like air pollution that have detrimental effects on asthma and respiratory illnesses also have an impact on COVID-19.

RECENT FINDINGS

Angiotensin-converting enzyme 2 (ACE2) is the receptor for the attachment and entry of SARS-CoV-2 into the host cells that is upregulated by Th1-mediated responses. In asthmatics, ACE2 gene expression is generally reduced and recent studies have shown a negative correlation between the levels of Th2 cytokines including IL-4, IL-5, and IL-13 in airway epithelial cells and other type 2 biomarkers with ACE2 expression. This may explain in part the potential protective role of asthma on COVID-19. Here, we review the relation of respiratory viral illnesses and asthma, the immune-molecular mechanisms of SARS-CoV-2 infection, the impact of asthma on COVID-19 and that of SARS-CoV-2 on asthma and allergic rhinitis, and the impact of environmental factors like air pollution on COVID-19.

SUMMARY

Expression of ACE2 in airway epithelial cells in SARS-COV-2 is influenced by inflammatory profile. Respiratory allergic diseases like asthma appear to have a protective effect against SARS-COV-2 infection. However, the clinical association between asthma and SARS-COV-2 is not fully established and the underlying immune-molecular mechanisms may explain these observations.

Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas

The spread of SARS-CoV-2, the beta coronavirus responsible for the current pneumonia pandemic outbreak, has been speculated to be linked to short-term and long-term atmospheric pollutants exposure. The present work has been aimed at analyzing the atmospheric pollutants concentrations (PM10, PM2.5, NO2) and spatio-temporal distribution of cases and deaths (specifically incidence, mortality and lethality rates) across the whole Italian national territory, down to the level of each individual territorial area, with the goal of checking any potential short-term correlation between these two phenomena. The data analysis has been limited to the first quarter of 2020 to reduce the lockdown-dependent biased effects on the atmospheric pollutant levels as much as possible. The analysis looked at non-linear, monotonic correlations using the Spearman non-parametric correlation index. The statistical significance of the Spearman correlations has also been evaluated. The results of the statistical analysis suggest the hypothesis of a moderate-to-strong correlation between the number of days exceeding the annual regulatory limits of PM10, PM2.5 and NO2 atmospheric pollutants and COVID-19 incidence, mortality and lethality rates for all the 107 territorial areas in Italy. A weak-to-moderate correlation seems to exist when considering the 36 territorial areas in four of the most affected regions (Lombardy, Piedmont, Emilia-Romagna and Veneto). Overall, PM10 and PM2.5 showed a higher non-linear correlation than NO2 with incidence, mortality and lethality rates. As to particulate matters, PM10 profile has been compared with the incidence rate variation that occurred in three of the most affected territorial areas in Northern Italy (i.e., Milan, Brescia, and Bergamo). All areas showed a similar PM10 time trend but a different incidence rate variation, that was less severe in Milan compared with Brescia and Bergamo.

Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model

Purpose

As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

Design/methodology/approach

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Findings

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

Originality/value

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.

Potential sources, modes of transmission and effectiveness of prevention measures against SARS-CoV-2

During the current SARS-CoV-2 pandemic new studies are emerging daily providing novel information about sources, transmission risks and possible prevention measures. In this review, we aimed to comprehensively summarize the current evidence on possible sources for SARS-CoV-2, including evaluation of transmission risks and effectiveness of applied prevention measures. Next to symptomatic patients, asymptomatic or pre-symptomatic carriers are a possible source with respiratory secretions as the most likely cause for viral transmission. Air and inanimate surfaces may be sources; however, viral RNA has been inconsistently detected. Similarly, even though SARS-CoV-2 RNA has been detected on or in personal protective equipment (PPE), blood, urine, eyes, the gastrointestinal tract and pets, these sources are currently thought to play a negligible role for transmission. Finally, various prevention measures such as handwashing, hand disinfection, face masks, gloves, surface disinfection or physical distancing for the healthcare setting and in public are analysed for their expected protective effect.

The Evolution of Covid-19 in Italy after the Spring of 2020: An Unpredicted Summer Respite Followed by a Second Wave

The coronavirus (COVID-19) pandemic was particularly invasive in Italy during the period between March and late April 2020, then decreased in both the number of infections and in the seriousness of the illness throughout the summer of 2020. In this work, we measure the severity of the disease by the ratio of Intensive Care Units (ICU) spaces occupied by COVID-19 patients and the number of Active Cases (AC) each month from April to October 2020. We also use the ratio of the number of Deaths (D) to the number of Active Cases. What clearly emerges, from rigorous statistical analysis, is a progressive decrease in both ratios until August, indicating progressive mitigation of the disease. This is particularly evident when comparing March-April with July-August; during the summer period the two ratios became roughly 18 times lower. We test such sharp decreases against possible bias in counting active cases and we confirm their statistical significance. We then interpret such evidence in terms of the well-known seasonality of the human immune system and the virus-inactivating effect of stronger UV rays in the summer. Both ratios, however, increased again in October, as ICU/AC began to increase in September 2020. These ratios and the exponential growth of infections in October indicate that the virus-if not contained by strict measures-will lead to unsustainable challenges for the Italian health system in the winter of 2020-2021.

CPAP management of COVID-19 respiratory failure: a first quantitative analysis from an inpatient service evaluation

Objective

To evaluate the role of continuous positive air pressure (CPAP) in the management of respiratory failure associated with COVID-19 infection. Early clinical management with limited use of CPAP (3% of patients) was compared with a later clinical management strategy which had a higher proportion of CPAP use (15%).

Design

Retrospective case-controlled service evaluation for a single UK National Health Service (NHS) Trust during March–June 2020 designed and conducted solely to estimate the effects of current care.

Setting

The acute inpatient unit in Wrightington, Wigan and Leigh Teaching Hospitals NHS Foundation Trust, a medium-sized English NHS Trust.

Participants

206 patients with antigen confirmed COVID-19 disease and severe acute respiratory syndrome admitted between 17 March 2020 and 3 April 2020 for the early group (controls), and between 10 April 2020 and 11 May 2020 for the late group (cases). Follow-up for all cases was until 11 June by which time all patients had a final outcome of death or discharge. Both groups were composed of 103 patients. Cases and controls were matched by age and sex.

Outcome measure

The outcome measure was the proportion of patients surviving at time t (time from the positive result of COVID-19 test to discharge/death date). The predictors were CPAP intervention, intubation, residence in care homes and comorbidities (renal, pulmonary, cardiac, hypertension and diabetes). A stratified Cox proportional hazard for clustered data (via generalised estimating equations) and model selection algorithms were employed to identify the effect of CPAP on patients’ survival and the effect on gas exchange as measured by alveolar arterial (A-a) gradient and timing of CPAP treatment on CPAP patients’ survival.

Results

CPAP was found to be significantly (HR 0.38, 95% CI 0.36 to 0.40) associated with lower risk of death in patients with hospital stay equal to, or below 7 days. However, for longer hospitalisation CPAP was found to be associated with increased risk of death (HR 1.72, 95% CI 1.40 to 2.12). When CPAP was initiated within 4 days of hospital admission, the survival probability was above 73% (95% CI 53% to 99%). In addition, lower A-a gradient was associated with lower risk of death in CPAP patients (HR 1.011, 95% CI 1.010 to 1.013). The selected model (best fit) was stratified by sex and clustered by case/control groups. The predictors were age, intubation, hypertension and the residency from care homes, which were found to be statistically significantly associated with patient’s death/discharge.

Conclusions

CPAP is a simple and cost-effective intervention. It has been established for care of other respiratory disorders but not for COVID-19 respiratory failure. This evaluation establishes that CPAP as a potentially viable treatment option for this group of patients during the first days of hospital admission. As yet there is limited availability of quantitative research on CPAP use for COVID-19. Whist this work is hampered by both the relatively small sample size and retrospective design (which reduced the ability to control potential confounders), it represents evidence of the significant benefit of early CPAP intervention. This evaluation should stimulate further research questions and larger study designs on the potential benefit of CPAP for COVID-19 infections. Globally, this potentially beneficial low cost and low intensity therapy could have added significance economically for healthcare provision in less developed countries.

Brazil before and during COVID-19 pandemic: Impact on the practice and habits of physical exercise

INTRODUCTION

The goal of this study was to investigate the practice and habits of physical exercise impact due to COVID-19 social isolation before and during the outbreak in Brazil and its mains macro-regions, which have social and economic disparities.

METHODS

This is an observational cross-sectional study through an online questionnaire survey. A self-administered survey (PEF-COVID19) was applied in the Brazilian general population, age ≥ 18 years. T-test for independent samples and Chi-square tests were used to compare Brazil and different macro-regions, p˂0.05.

RESULTS

1,977 participants filled the survey and 1,845 were included; 80% of the included ones were in social isolation. In general, the self-related physical exercise practice before and during the COVID-19 pandemic and exercise characteristics were highly affected by the pandemic (p˂0.05). Sedentary behavior increased significantly (14.9 vs. 29.8%) and between the active ones, the frequency of PEx per week and duration of the PEx practice decreased significantly. The motivation to exercise also changed considering performance and health before and during the pandemic, respectively (10 vs. 5.6% and 72.4 vs. 79.1%). Also, the sample started to exercise less in the night and more in the afternoon, respectively (33.7 vs. 25.3% and 19.1 vs. 31.5%).

CONCLUSIONS

The COVID-19 pandemic highly influenced the physical exercise practice and habits (frequency, duration, motivation, period to exercise) in Brazil and its main macro-regions. These results can be used to create measures, as home-based exercise programs, to avoid the harm of sedentary behaviors and mental health impact during and after the pandemic.

Global impact of environmental temperature and BCG vaccination coverage on the transmissibility and fatality rate of COVID-19

The 2019-Coronavirus (COVID-19) pandemic has had a global impact. The effect of environmental temperature on transmissibility and fatality rate of COVID-19 and protective efficacy of Bacillus Calmette-Guérin (BCG) vaccination towards COVID-19 remains ambiguous. Therefore, we explored the global impact of environmental temperature and neonatal BCG vaccination coverage on transmissibility and fatality rate of COVID-19. The COVID-19 data for reported cases, deaths and global temperature were collected from 31^st December 2020 to 3^rd April 2020 for 67 countries. Temperature data were split into quartiles for all three categories (minimum temperature, maximum temperature and mean temperature). The impact of three types of temperature data and policy of BCG vaccination on COVID-19 infection was determined by applying the multivariable two-level negative binomial regression analysis keeping daily new cases and daily mortality as outcome. The highest number of cases fell in the temperature categories as following: mean temperature in the second quartile (6°C to 10.5°C), median 26, interquartile range (IQR) 237; minimum temperature in the first quartile (-26°C to 1°C), median 23, IQR 173; maximum temperature in the second quartile (10°C to 16°C), median 27.5, IQR 219. For the minimum temperature category, 28% statistically significant lower incidence was noted for new cases from the countries falling in the second quartile (2°C to 6°C) compared with countries falling in the first quartile (-26°C to 1°C) (incidence rate ratio [IRR] 0.72, 95% confidence interval [CI] 0.57 to 0.93). However, no statistically significant difference in incidence rate was observed for mean temperature categories in comparison to the first quartile. Countries with BCG vaccination policy had 58% less mortality as compared with countries without BCG coverage (IRR 0.42; 95% CI 0.18 to 0.95). Our exploratory study provides evidence that high temperature might not be associated with low transmissibility and countries having neonatal BCG vaccination policy had a low fatality rate of COVID-19.

Poor knowledge of COVID-19 and unfavourable perception of the response to the pandemic by healthcare workers at the Bafoussam Regional Hospital (West Region-Cameroon)

INTRODUCTION

WHO warned against a dramatic impact of COVID-19 in Africa unless adequate response strategies are implemented. Whatever the strategy, the role of health staff is pivotal. Objective: Assess knowledge and perception of the response to COVID-19 among health staff.

METHODS

we used a convenience non probabilistic sample to conduct a survey with a self-administered questionnaire from April 14 - 29, 2020 at the Bafoussam Regional Hospital (BRH). All the staff was invited to participate. Analyses were done with Microsoft Excel 2010 and Epi-Info version 7.1.5.2.

RESULTS

response rate was 76.1% (464/610). Mean age (SD) was 35.0 (8.9) years. Sex ratio (M/F) was 101/356. Nursing/midwifery staff (56.8%) and in-patients units (49.94%) were predominant. Origin and transmission of SARS-CoV-2 were poorly known while knowledge of clinical signs and the role of laboratory tests were good. For 53.2% of respondents all therapeutic regimens are supportive and only 31.6% trusted state-recommended drugs. For 169 of respondents (36.9%), herbal remedies prevent/cure COVID-19. Seventy percent (70%) felt they were not knowledgeable enough to handle COVID-19 cases. Eighty-five point six percent (85.6%) thought the BRH had insufficient resources to fight COVID-19 and 55.6% were dissatisfied with its response (weaknesses: medicines/technologies (74.5%), service delivery (28.1%), staff (10.9%)). Sixty-eight percent (68%) reported insufficient protection on duty. Seventy-six point five percent (76.5%) reported a drop in non-COVID-19 services. Eighty-five point five (85.5%) percent said they complied with community preventive measures. For 44% of respondents, regulations on COVID-19 corpses should be made more culture-sensitive. Fifty one point two percent (51.2%) of respondents were against vaccine trial in their community.

CONCLUSION

knowledge was poor and perception of the response to COVID-19 was unfavourable.

Preliminary Analysis of Relationships between COVID19 and Climate, Morphology, and Urbanization in the Lombardy Region (Northern Italy)

The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics - such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. Both the Mann–Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1.5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy.

May SARS-CoV-2 Diffusion Be Favored by Alkaline Aerosols and Ammonia Emissions?

Ammonia is a common factor linking air in bat caves and air pollution in the proximity of agricultural fields treated with livestock farming sewage and slaughterhouses, where important clusters of COVID-19 have recently been reported all over the world. Such a commonality has a further connection with the known behavior of some viruses of the coronavirus family, such as the murine hepatitis virus, whose spike glycoprotein (S) can be triggered to a membrane-binding conformation at pH 8.0. Within the airborne route of virus transmission, with particular relevance for crowded and enclosed environments, these observations have prompted a hypothesis that may represent a contributing cause to interpret the geographical variability of the virus diffusion and the surging rise of COVID-19 cases in slaughterhouses all over the world. The hypothesis is that, in these environments, the SARS-CoV-2 S protein may find on a fraction of the airborne particles an alkaline pH, favorable to trigger the conformational changes, needed to induce the fusion of the viral envelope with the plasma membrane of the target cells.

Prospects for SARS-CoV-2 diagnostics, therapeutics and vaccines in Africa

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic, prompting unprecedented efforts to contain the virus. Many developed countries have implemented widespread testing and have rapidly mobilized research programmes to develop vaccines and therapeutics. However, these approaches may be impractical in Africa, where the infrastructure for testing is poorly developed and owing to the limited manufacturing capacity to produce pharmaceuticals. Furthermore, a large burden of HIV-1 and tuberculosis in Africa could exacerbate the severity of infection and may affect vaccine immunogenicity. This Review discusses global efforts to develop diagnostics, therapeutics and vaccines, with these considerations in mind. We also highlight vaccine and diagnostic production platforms that are being developed in Africa and that could be translated into clinical development through appropriate partnerships for manufacture.

The COVID-19 pandemic has prompted unparalleled progress in the development of vaccines and therapeutics in many countries, but it has also highlighted the vulnerability of resource-limited countries in Africa. Margolin and colleagues review global efforts to develop SARS-CoV-2 diagnostics, therapeutics and vaccines, with a focus on the opportunities and challenges in Africa.

Variations in climate and global spread of COVID-19: Implications for control in tropical and warmer climates

Health and climatic changes have been linked for centuries. Viruses, bacteria, fungi and parasites are sensitive to environmental conditions including ambient temperature, humidity, smog, and other climatic factors like exposure to sunlight. Viral respiratory tract infections such as seasonal influenza are known to become epidemic primarily during cold weather conditions influenced also by population density and travel patterns. The winter months in China are from November to March. The SARS-CoV-1 outbreak commenced in Guangdong Province, in November 2002 and the SARS-CoV-2 outbreak in Wuhan in early December 2019. Understanding how the emergence and spread of COVID-19 could be affected by climatic conditions may be an important aspect for disease control. As of August 4th 2020, 18 142 718 confirmed cases and 691 013 deaths have been reported to the WHO from all continents [...].

Association of Environmental Parameters with COVID-19 in Delhi, India

The present study explores the association between weather and COVID-19 pandemic in Delhi, India. The study used the data from daily newspaper releases from the Ministry of Health and Family Welfare, Government of India. Linear regression was run to understand the effect of the number of tests, temperature, and relative humidity on the number of COVID-19 cases in Delhi. The model was significantly able to predict number of COVID-19 cases, F (4,56) = 1213.61, p < 0.05, accounting for 99.4% of the variation in COVID-19 cases with adjusted R² = 98.8%. Maximum Temperature, average temperature and average relative humidity did not show statistical significance. The only number of tests was significantly associated with COVID-19 cases.

The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting

Over the past two decades, there have been two major outbreaks where the crossover of animal Betacoronaviruses to humans has resulted in severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In December 2019, a global public health concern started with the emergence of a new strain of coronavirus (SARS-CoV-2 or 2019 novel coronavirus, 2019-nCoV) which has rapidly spread all over the world from its origin in Wuhan, China. SARS-CoV-2 belongs to the Betacoronavirus genus, which includes human SARS-CoV, MERS and two other human coronaviruses (HCoVs), HCoV-OC43 and HCoV-HKU1. The fatality rate of SARS-CoV-2 is lower than the two previous coronavirus epidemics, but it is faster spreading and the large number of infected people with severe viral pneumonia and respiratory illness, showed SARS-CoV-2 to be highly contagious. Based on the current published evidence, herein we summarize the origin, genetics, epidemiology, clinical manifestations, preventions, diagnosis and up to date treatments of SARS-CoV-2 infections in comparison with those caused by SARS-CoV and MERS-CoV. Moreover, the possible impact of weather conditions on the transmission of SARS-CoV-2 is also discussed. Therefore, the aim of the present review is to reconsider the two previous pandemics and provide a reference for future studies as well as therapeutic approaches.

A Look Behind the Scenes at COVID-19: National Strategies of Infection Control and Their Impact on Mortality

(1) Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began spreading across the globe in December and, as of 9 July 2020, had inflicted more than 550,000 deaths. Public health measures implemented to control the outbreak caused socio-economic havoc in many countries. The pandemic highlighted the quality of health care systems, responses of policymakers in harmony with the population, and socio-economic resilience factors. We suggest that different national strategies had an impact on mortality and case count. (2) Methods: We collected fatality data for 17 countries until 2 June 2020 from public data and associated these with implemented containment measures. (3) Results: The outcomes present the effectiveness of control mechanisms in mitigating the virus for selected countries and the UAE as a special case. Pre-existing conditions defined the needed public health strategies and fatality numbers. Other pre-existing conditions, such as temperature, humidity, median age, and low serum 25-hydroxyvitamin D (25(OH)D) concentrations played minor roles and may have had no direct impact on fatality rates. (4) Conclusions: Prevention, fast containment, adequate public health strategies, and importance of indoor environments were determining factors in mitigating the pandemic. Development of public health strategies adapted to pre-existing conditions for each country and community compliance with implemented policies ensure the successful control of pandemics.

Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health

The study described in this manuscript analyzed the effects of quarantine and social distancing policies implemented due to the Coronavirus Disease 2019 (COVID-19) pandemic on air pollution levels in four western megacities: São Paulo in Brazil; Paris in France; and Los Angeles and New York in the United States. The study investigated the levels of four air pollutants-Carbon monoxide (CO), Ozone (O3), Fine Particulate (PM2.5) and Nitrogen dioxide (NO2)-during the month of March 2020, compared to 2015-2019, in the urban air of these metropolitan areas, controlling for meteorological variables. Results indicated reductions in the levels of PM2.5, CO and NO2, with reductions of the latter two showing statistical significance. In contrast, tropospheric ozone levels increased, except in Los Angeles. The beneficial health effects of cleaner air might also help prevent deaths caused by the epidemic of COVID-19 in megacities by diminishing pressure on hospitals and health equipment. Future actions for the re-starting of non-essential economic activities in these cities should take into consideration the overall importance of health for the individual, as well as for societies.

Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0-34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23-42%) after the first set of restrictions, and by 80% (95%CI = 70-89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate

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A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate.

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Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO₂ emission and population density) are used in the model.

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The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China.

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A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase.

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The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results.

COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.

In this paper, infection rate of COVID-19 is modelled globally at a 0.5^∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO² and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries’ reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment.

The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.