Global prediction system for COVID-19 pandemic

Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015-2022 using a deep learning model

As one of the significant air pollutants, nitrogen oxides (NOx = NO + NO2) not only pose a great threat to human health, but also contribute to the formation of secondary pollutants such as ozone and nitrate particles. Due to substantial uncertainties in bottom-up emission inventories, simulated concentrations of air pollutants using GEOS-Chem model often largely biased from those of ground-level observations. To address this issue, we developed a new deep learning model to simulate the inverse process of the GEOS-Chem model. This framework was applied to improve the total anthropogenic NOx emission intensity over a five-year period (2015-2019), and then to predict anthropogenic NOx emission intensity for 2020-2022. The deep learning model showed higher R2 value (0.94) based on 10-fold cross-validation, indicating that the model effectively captured spatial features and patterns. Then, NOx emission intensity was calibrated based on the new framework using high-resolution NO2 concentration dataset instead of the simulated NO2 levels derived from GEOS-Chem model. Overall, the top-down inversion result was in agreement with the bottom-up emission inventory at the spatial scale. The top-down emission fluxes were lower in high-emission regions such as central China, Beijing, Shanghai, the Pearl River Delta, and the central part of Liaoning, while posterior estimates were higher in regions with lower prior emission intensity. The posterior NOx emission intensity suggested that some major regions such as Beijing-Tianjin-Hebei (BTH) (-56.4 %) and Pearl River Delta (PRD) (-52.5 %) experienced dramatic NOx emission intensity decreases from 2015 to 2022, whereas some remote regions such as Tibetan Plateau remained relatively stable. This research contributes to the timely tracking of changes in pollutant emission and aids in the formulation of more effective and relevant pollution prevention and control policies.

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Development of the second version of Global Prediction System for Epidemiological Pandemic

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

Global prediction for mpox epidemic

The mpox epidemic had spread worldwide and become an epidemic of international concern. Before the emergence of targeted vaccines and specific drugs, it is necessary to numerically simulate and predict the epidemic. In order to better understand and grasp its transmission situation, and take some countermeasures accordingly when necessary, we predicted and simulated mpox transmission, vaccination and control scenarios using model developed for COVID-19 predictions. The results show that the prediction model can also achieve good results in predicting the mpox epidemic based on modified SEIR model. The total number of people infected with mpox on Dec 31, 2022 reached 83878, while the prediction of the model was 96456 with a relative error of 15%. The United States, Brazil, Spain, France, the United Kingdom and Germany are six countries with serve mpox epidemic. The predictions of their epidemic are 30543, 11191, 7447, 5945, 5606 and 4291 cases respectively, with an average relative error of 20%. If 30% of the population is vaccinated using a vaccine that is 78% effective, the number of infected people will drop by 29%. This shows that the system can be practically applied to the prediction of mpox epidemic and provide corresponding decision-making reference.

Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis

BACKGROUND

The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the "dynamic zero-COVID" policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions.

METHODS

Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period.

RESULTS

(1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature. (2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days. (3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90-1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4-6 days in more than 50% of mass outbreaks. (4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping.

CONCLUSION

Overall, our findings suggest the variation in the infection rates during the "dynamic zero-COVID" policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.

Early monitoring-to-warning Internet of Things system for emerging infectious diseases via networking of light-triggered point-of-care testing devices

Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample-to-answer genetic diagnosis platform based on light-controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30 seconds. Then, on site polymerase chain reaction was performed in a handheld closed-loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point-of-care testing. Additionally, through multiple device networking, a real-time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti-epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.

Impact of human mobility on the epidemic spread during holidays

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures.

Effect of temperature on fast transmission of COVID-19 in low per capita GDP Asian countries

An abrupt outbreak of COVID-19 caused enormous global concerns. Although all countries around the world are severely affected, developing Asian countries faced more difficulties due to their low per capita GDP. The temperature was considered a leading variable in spreading viral diseases, including COVID-19. The present study aimed to assess the relationship between temperature and the spread of COVID-19, with a focus on developing Asian countries. In a few Asian countries, COVID-19 spread rapidly in the summer, while in some countries, there is an increase in winter. A linear correlation was developed between COVID-19 cases/deaths and temperature for the selected countries, which were very weak. A coefficient of determination of 0.334 and 0.365 was observed between cases and average monthly max/min temperatures. A correlation of R2 = 0.307 and 0.382 was found between deaths and average max/min monthly temperatures, respectively. There is no scientific reason to assume that COVID-19 is more dominant at low than high temperatures. Therefore, it is believed that the results may be helpful for the health department and decision-makers to understand the fast spread of COVID-19.

Heat waves accelerate the spread of infectious diseases

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case

BACKGROUND

The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result.

METHODS

The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022.

RESULTS

Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively.

CONCLUSIONS

The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures' impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Assessment Model for Rapid Suppression of SARS-CoV-2 Transmission under Government Control

The rapid suppression of SARS-CoV-2 transmission remains a priority for maintaining public health security throughout the world, and the agile adjustment of government prevention and control strategies according to the spread of the epidemic is crucial for controlling the spread of the epidemic. Thus, in this study, a multi-agent modeling approach was developed for constructing an assessment model for the rapid suppression of SARS-CoV-2 transmission under government control. Different from previous mathematical models, this model combines computer technology and geographic information system to abstract human beings in different states into micro-agents with self-control and independent decision-making ability; defines the rules of agent behavior and interaction; and describes the mobility, heterogeneity, contact behavior patterns, and dynamic interactive feedback mechanism of space environment. The real geospatial and social environment in Taiyuan was considered as a case study. In the implemented model, the government agent could adjust the response level and prevention and control policies for major public health emergencies in real time according to the development of the epidemic, and different intervention strategies were provided to improve disease control methods in the simulation experiment. The simulation results demonstrate that the proposed model is widely applicable, and it can not only judge the effectiveness of intervention measures in time but also analyze the virus transmission status in complex urban systems and its change trend under different intervention measures, thereby providing scientific guidance to support urban public health safety.

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97-0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97-0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26-2.74) and 1.078 (95%CI 1.04-1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93-1.31) and 1.007 (95%CI 0.99-1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

Mathematical appraisal of SARS-CoV-2 Omicron epidemic outbreak in unprecedented Shanghai lockdown

The SARS-CoV-2 Omicron outbreak is ongoing in Shanghai, home to 25 million population. Here, we presented a novel mathematical model to evaluate the Omicron spread and Zero-COVID strategy. Our model provided important parameters, the average quarantine ratio, the detection interval from being infected to being tested positive, and the spreading coefficient to understand the epidemic progression better. Moreover, we found that the key to a relatively accurate long-term forecast was to take the variation/relaxation of the parameters into consideration based on the flexible execution of the quarantine policy. This allowed us to propose the criteria for estimating the parameters and outcome for the ending stage that is likely to take place in late May. Altogether, this model helped to give a correct mathematical appraisal of the SARS-CoV-2 Omicron outbreak under the strict Zero-COVID policy in China.

Statistical model for factors correlating with COVID-19 deaths

Background

The COVID-19 pandemic has caused major disruption in societies globally. Our aim is to understand, what factors were associated with the impact of the pandemic on death rates. This will help countries to better prepare for and respond in future pandemics.

Methods

We modeled with a linear mixed effect model the impact of COVID-19 with the dependent variable "Daily mortality change" (DMC) with country features variables and intervention (containment measurement) data. We tested both country characteristics consisting of demographic, societal, health related, healthcare system specific, environmental and cultural feature as well as COVID-19 specific response in the form of social distancing interventions.

Results

A statistically significant country feature was Geert Hofstede's masculinity, i.e., the extent to which the use of force is endorsed socially, correlating positively with a higher DMC. The effects of different interventions were stronger that those of country features, particularly cancelling public events, controlling international travel and closing workplaces.

Conclusion

Social distancing interventions and the country feature: Geert Hofstede's masculinity dimension had a significant impact on COVID-19 mortality change. However other country features, such as development and population health did not show significance. Thus, the crises responders and scholars could revisit the concept and understanding of preparedness for and response to pandemics.

Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate: A Google Earth Engine-Based Remote Sensing Approach

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

A novel method to detect the early warning signal of COVID-19 transmission

BACKGROUND

Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It's necessary to forecast early warning signals of potential outbreaks of COVID-19, which would facilitate the health ministry to take some suitable control measures timely to prevent or slow the spread of COVID-19. However, since the intricacy of COVID-19 transmission, which connects biological and social systems, it is a difficult task to predict outbreaks of COVID-19 epidemics timely.

RESULTS

In this work, we developed a new model-free approach, called, the landscape network entropy based on Auto-Reservoir Neural Network (ARNN-LNE), for quantitative analysis of COVID-19 propagation, by mining dynamic information from regional networks and short-term high-dimensional time-series data. Through this approach, we successfully identified the early warning signals in six nations or areas based on historical data of COVID-19 infections.

CONCLUSION

Based on the newly published data on new COVID-19 disease, the ARNN-LNE method can give early warning signals for the outbreak of COVID-19. It's worth noting that ARNN-LNE only relies on small samples data. Thus, it has great application potential for monitoring outbreaks of infectious diseases.

Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies

Objective

Predicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.

Materials and Methods

We extracted 2093 features (5 from the US COVID-19 case number history, 1824 from the demographic characteristics independently/interdependently, and 264 from the social distancing policies independently/interdependently) for 3142 US counties. Using the top selected 200 features, we built 4 machine learning models: Logistic Regression, Naïve Bayes, Multi-Layer Perceptron, and Random Forest, along with 4 Ensemble methods: Average, Product, Minimum, and Maximum, and compared their performances.

Results

The Ensemble Average method had the highest area-under the receiver operator characteristic curve (AUC) of 0.692. The top ranked features were all interdependent features.

Conclusion

The findings of this study suggest the predictive power of diverse features, especially when combined, in predicting county-level trends of COVID-19 cases and can be helpful to individuals in making their daily decisions. Our results may guide future studies to consider more features interdependently from conventionally distinct data sources in county-level predictive models. Our code is available at: https://doi.org/10.5281/zenodo.6332944.

Mechanism of Huo-Xiang-Zheng-Qi in Preventing and Treating COVID-19: A Study Based on Network Pharmacology and Molecular Docking Techniques

Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection; however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.

Water Transmission Increases the Intensity of COVID-19 Outbreaks

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

The Impact and Evaluation of the COVID-19 Pandemic on the Teaching of Biology from the Perspective of Slovak School Teachers

The closing of schools due to COVID-19 was a critical incident that should have caused a rethinking of education in our country. Among the many changes that this crisis has brought, one is fully remote teaching. Our research focuses on a comparison of the changes between on-site and remote forms of biology teaching, the opinions and feelings of teaching staff across all the institutional levels, and their opinions regarding the usage of online teaching tools in the future. The research shows that teachers have used both time-tested teaching aids and modern technology to generate an environment that would be as close to on-site teaching as possible. Similarly, the teachers with longer teaching experience had felt a greater degree of stress during the remote teaching period. Teachers of primary and tertiary schools agree that they can imagine having a combined form of education in the future but that the practical classes of biology must be completed on-site. On the other hand, most secondary school teachers want to preserve only the on-site form of teaching. Our study provides information on the current state of coping with the pandemic situation in Slovakia from teachers’ perspectives.

The global transmission of new coronavirus variants

Coronavirus Disease 2019 (COVID-19) has caused tremendous losses to the world. This study addresses the impact and diffusion of the five major new coronavirus variants namely Alpha, Beta, Gamma, Eta, and Delta lineage. The results of this study indicate that Africa and Europe will be affected by new coronavirus variants the most compared with other continents. The comparative analysis indicates that vaccination can contain the spread of the virus in most of the continent, and non-pharmaceutical interventions (NPIs), such as restriction on gatherings and close public transport, will effectively curb the pandemic, especially in densely populated continents. According to our Global Prediction System of COVID-19 Pandemic, the diffusion of delta lineage in the US shows seasonal oscillation characteristics, and the first wave will occur in October 2021, with the record of 323,360, and followed by a small resurgence in April 2022, with the record of 184,196, while the second wave will reach to 232,622 cases in October 2022. Our study will raise the awareness of new coronavirus variants among the public, and will help the governments make appropriate directives to cope with the new coronavirus variants.

How Should the Structure of Smart Cities Change to Predict and Overcome a Pandemic?

A proposed countermeasure to COVID-19 is a robust healthcare system that can respond and identify transmission paths using information technology. This involves the use of smart city services for tracking an infected person. However, during the COVID-19 pandemic, the healthcare system could only provide data on the number of infected people. Additionally, smart city services could respond neither timely nor sequentially. This study proposed a method for timely and sequential responses, through a flexible combination of the healthcare system and smart city services by envisioning a scenario that sequentially grafts the current status of COVID-19 in Korea. The results are the following. First, the COVID-19 outbreak was summarized in the context of the healthcare system and current smart city services. A method by which the latter could respond to the various needs of the former was suggested. Second, recommendations on combining or dismissing certain smart city services, as per the needs of coping with COVID-19, were summarized. Third, smart city services must be utilized only for addressing pandemics, as data from the healthcare system consists of personal information. Therefore, smart city services for responding to COVID-19 must be flexible.

Analysis of non-pharmaceutical interventions impacts on COVID-19 pandemic in Iran

The COVID-19 pandemic shows to have a huge impact on people's health and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. The Iranian authorities applied various non-pharmaceutical interventions to eradicate the epidemic in different periods. This study aims to investigate the effectiveness of non-pharmaceutical interventions in managing the current Coronavirus pandemic and to predict the next wave of infection in Iran. To achieve the research objective, the number of cases and deaths before and after the interventions was studied and the effective reproduction number of the infection was analyzed under various scenarios. The SEIR generic model was applied to capture the dynamic of the pandemic in Iran. To capture the effects of different interventions, the corresponding reproduction number was considered. Depending on how people are responsive to interventions, the effectiveness of each intervention has been investigated. Results show that the maximum number of the total of infected individuals will occur around the end of May and the start of June 2021. It is concluded that the outbreak could be smoothed if full lockdown and strict quarantine continue. The proposed modeling could be used as an assessment tool to evaluate the effects of different interventions in new outbreaks.

A death, infection, and recovery (DIR) model to forecast the COVID-19 spread

BACKGROUND

The SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate.

OBJECTIVE

In this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc.

METHOD

The size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE).

RESULTS

Experimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%.

CONCLUSION

This percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.

Environmental spatial heterogeneity of the impacts of COVID-19 on the top-20 metropolitan cities of Asia-Pacific

This study investigated the environmental spatial heterogeneity of novel coronavirus (COVID-19) and spatial and temporal changes among the top-20 metropolitan cities of the Asia-Pacific. Remote sensing-based assessment is performed to analyze before and during the lockdown amid COVID-19 lockdown in the cities. Air pollution and mobility data of each city (Bangkok, Beijing, Busan, Dhaka, Delhi, Ho Chi Minh, Hong Kong, Karachi, Mumbai, Seoul, Shanghai, Singapore, Tokyo, Wuhan, and few others) have been collected and analyzed for 2019 and 2020. Results indicated that almost every city was impacted positively regarding environmental emissions and visible reduction were found in Aerosol Optical Depth (AOD), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) concentrations before and during lockdown periods of 2020 as compared to those of 2019. The highest NO2 emission reduction (~ 50%) was recorded in Wuhan city during the lockdown of 2020. AOD was highest in Beijing and lowest in Colombo (< 10%). Overall, 90% movement was reduced till mid-April, 2020. A 98% reduction in mobility was recorded in Delhi, Seoul, and Wuhan. This analysis suggests that smart mobility and partial shutdown policies could be developed to reduce environmental pollutions in the region. Wuhan city is one of the benchmarks and can be replicated for the rest of the Asian cities wherever applicable.

Modelling the SARS-CoV-2 outbreak: Assessing the usefulness of protective measures to reduce the pandemic at population level

A new bioinspired computational model was developed for the SARS-CoV-2 pandemic using the available epidemiological information, high-resolution population density data, travel patterns, and the average number of contacts between people. The effectiveness of control measures such as contact reduction measures, closure of communities (lockdown), protective measures (social distancing, face mask wearing, and hand hygiene), and vaccination were modelled to examine possibilities for control of the disease under several protective vaccination levels in the population. Lockdown and contact reduction measures only delay the spread of the virus in the population because it resumes its previous dynamics as soon as the restrictions are lifted. Nevertheless, these measures are probably useful to avoid hospitals being overwhelmed in the short term. Our model predicted that 56% of the Spanish population would have been infected and subsequently recovered over a 130 day period if no protective measures were taken but this percentage would have been only 34% if protective measures had been put in place. Moreover, this percentage would have been further reduced to 41.7, 27.7, and 13.3% if 25, 50 and 75% of the population had been vaccinated, respectively. Finally, this percentage would have been even lower at 25.5, 12.1 and 7.9% if 25, 50 and 75% of the population had been vaccinated in combination with the application of protective measures, respectively. Therefore, a combination of protective measures and vaccination would be highly efficacious in decreasing not only the number of those who become infected and subsequently recover, but also the number of people who die from infection, which falls from 0.41% of the population over a 130 day period without protective measures to 0.15, 0.08 and 0.06% if 25, 50 and 75% of the population had been vaccinated in combination with protective measures at the same time, respectively.

Marginal warming associated with a COVID-19 quarantine and the implications for disease transmission

During January-February 2020, parts of China faced restricted mobility under COVID-19 quarantines, which have been associated with improved air quality. Because particulate pollutants scatter, diffuse, and absorb incoming solar radiation, a net negative radiative forcing, decreased air pollution can yield surface warming. As such, this study (1) documents the evolution of China's January-February 2020 air temperature and concurrent particulate changes; (2) determines the temperature response related to reduced particulates during the COVID-19 quarantine (C19Q); and (3) discusses the conceptual implications for temperature-dependent disease transmission. C19Q particulate evolution is monitored using satellite analyses, and concurrent temperature anomalies are diagnosed using surface stations and Aqua AIRS imagery. Meanwhile, two WRF-Chem simulations are forced by normal emissions and the satellite-based urban aerosol changes, respectively. Urban aerosols decreased from 27.1% of pre-C19Q aerosols to only 17.5% during C19Q. WRF-Chem resolved ~0.2 °C warming across east-central China, that represented a minor, though statistically significant contribution to C19Q temperature anomalies. The largest area of warming is concentrated south of Chengdu and Wuhan where temperatures increased between +0.2-0.3 °C. The results of this study are important for understanding the anthropogenic forcing on regional meteorology. Epidemiologically, the marginal, yet persistent, warming during C19Q may retard temperature-dependent disease transmission, possibly including SARS-CoV-2.

Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition (EEMD) and autoregressive moving average (ARMA) model in a hybrid approach 基于集合经验模态分解和自回归-移动平均模型的 COVID-19 流行病全球预测系统预测结果改进

In 2020, the COVID-19 pandemic spreads rapidly around the world. To accurately predict the number of daily new cases in each country, Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic (GPCP). In this article, the authors use the ensemble empirical mode decomposition (EEMD) model and autoregressive moving average (ARMA) model to improve the prediction results of GPCP. In addition, the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease, whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model. Judging from the results, the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP. For countries such as El Salvador with a small number of cases, the absolute values of the relative errors of prediction become smaller. Therefore, this article concludes that this method is more effective for improving prediction results and direct prediction.

摘要

2020年, 新型冠状病毒肺炎 (COVID-19) 在世界范围内迅速传播.为准确预测各国每日新增发病人数, 兰州大学开发了 COVID-19 流行病全球预测系统 (GPCP). 在本文的研究中, 我们使用集合经验模态分解 (EEMD) 模型和自回归-移动平均 (ARMA) 模型对 GPCP 的预测结果进行改进, 并对发病人数较少或处于发病初期, 不完全符合传染病规律, GPCP 模型无法预测的国家进行直接预测.从结果来看, 使用该方法修正预测结果, 古巴等国家预测误差均大幅下降, 且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测, 相对误差较小, 预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.

The Pandemic Holiday Blip in New York City

When it comes to pandemics, such as the one caused by the Coronavirus disease COVID-19, various issues and problems have arisen for the healthcare infrastructure and institutions. With increasing number of patients in need of urgent medical care and hospitalizations, the healthcare systems and regional hospitals may approach their maximum service capacity and may face shortage of various parameters, such as supplies including PPE, medications, therapeutic devices, ventilators, beds, and many more. The article at hand describes the development and framework of a simulation model that enables the modeling and evaluation of the COVID-19 pandemic progress. To achieve this, the model dynamically mimics and simulates the developments and time-dependent behavior of various crucial parameters of the pandemic, among others, the daily infection numbers and death rate. In addition, the model enables the simulation of single events and scenarios that occur outside of the regular pandemic developments as anomalies, such as holidays. Unlike traditional models, the proposed framework is based on factors and parameters closely derived from reality, such as the contact rate of individuals, which allows for a much more realistic representation. In addition, the real connection enables the assessment of effects of various influences regarding the development and progress of the pandemic, such as hospitalization numbers over time. All the aforementioned points are possible within the simulation framework and do not require awaiting the unfolding of the effects in reality. Thus, the model is capable of dynamically predicting how different scenarios turn out. The abilities of the model are demonstrated, illustrated, and proven in a specific case study that shows the impact of holidays, such as Passover and Easter in New York City when quarantine measures might have been ignored, and an increase in extended family gatherings temporarily occurred. As a result, the simulation showed significant impacts and disproportionate number of patients in need of medical care that could be potentially detrimental in reality. For example, compared to the previous trajectory of the pandemic, for a temporary increase of 50% in the contact rate of individuals, the model showed that the total number of cases would increase by 461 090, the maximum number of required hospitalizations would rise to 79 733, and the total number of fatalities would climb by 19 125 over 90 days. In addition to its function and proven capabilities, the model can and is furthermore planned to be adapted to other areas, not necessarily only metropolitan regions in order to expand the utilization of its predictive power. Such predictions could be used to derive regulatory measures and to test various policies for COVID-19 containment.

Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage

In order to control the spread of COVID-19, China had implemented strict lockdown measures. The closure of cities had had a huge impact on human production and consumption activities, which had greatly reduced population mobility. This article used air pollutant data from 341 cities in mainland China and divided these cities into seven major regions based on geographic conditions and climatic environment. The impact of urban blockade on air quality during COVID-19 was studied from the perspectives of time, space, and season. In addition, this article used Normalized Difference Vegetation Index (NDVI) to systematically analyze the characteristics of air pollution in the country and used the Pearson correlation coefficient to explore the relationship between NDVI and the air pollutant concentrations during the COVID-19 period. Then, linear regression was used to find the quantitative relationship between NDVI and AQI, and the fitting effect of the model was found to be significant through t test. Finally, some countermeasures were proposed based on the analysis results, and suggestions were provided for improving air quality. This paper has drawn the following conclusions: (1) the concentration of pollutants varied greatly in different regions, and the causes of their pollution sources were also different. The region with the largest decline in AQI was the Northeast China (60.01%), while the AQI in the southwest China had the smallest change range, and its value had increased by 1.72%. In addition, after the implementation of the city blockade, the concentration of NO2 in different regions dropped the most, but the increase in O3 was more obvious. (2) Higher vegetation coverage would have a beneficial impact on the atmospheric environment. Areas with higher NDVI values have relatively low AQI. There is a negative correlation between NDVI and AQI, and an average increase of 0.1 in NDVI will reduce AQI by 3.75 (95% confidence interval). In the case of less human intervention, the higher the vegetation coverage, the lower the local pollutant concentration will be. Therefore, the degree of vegetation coverage would have a direct or indirect impact on air pollution.

Modeling of Vaccination and Contact Tracing as Tools to Control the COVID-19 Outbreak in Spain

We developed an agent-based stochastic model, based on P Systems methodology, to decipher the effects of vaccination and contact tracing on the control of COVID-19 outbreak at population level under different control measures (social distancing, mask wearing and hand hygiene) and epidemiological scenarios. Our findings suggest that without the application of protection social measures, 56.1% of the Spanish population would contract the disease with a mortality of 0.4%. Assuming that 20% of the population was protected by vaccination by the end of the summer of 2021, it would be expected that 45% of the population would contract the disease and 0.3% of the population would die. However, both of these percentages are significantly lower when social measures were adopted, being the best results when social measures are in place and 40% of contacts traced. Our model shows that if 40% of the population can be vaccinated, even without social control measures, the percentage of people who die or recover from infection would fall from 0.41% and 56.1% to 0.16% and 33.5%, respectively compared with an unvaccinated population. When social control measures were applied in concert with vaccination the percentage of people who die or recover from infection diminishes until 0.10% and 14.5%, after vaccinating 40% of the population. Vaccination alone can be crucial in controlling this disease, but it is necessary to vaccinate a significant part of the population and to back this up with social control measures.

Observed and estimated prevalence of Covid-19 in Italy: How to estimate the total cases from medical swabs data

During the Covid-19 pandemic in Italy, official data are collected with medical swabs following a pure convenience criterion which, at least in an early phase, has privileged the exam of patients showing evident symptoms. However, there are evidences of a very high proportion of asymptomatic patients. In this situation, in order to estimate the real number of infected (and to estimate the lethality rate), it should be necessary to run a properly designed sample survey through which it would be possible to calculate the probability of inclusion and hence draw sound probabilistic inference. Unfortunately, the survey run by the Italian Statistical Institute encountered many field difficulties. Some researchers proposed estimates of the total prevalence based on various approaches, including epidemiologic models, time series and the analysis of data collected in countries that faced the epidemic in earlier times. In this paper, we propose to estimate the prevalence of Covid-19 in Italy by reweighting the available official data published by the Istituto Superiore di Sanità so as to obtain a more representative sample of the Italian population. Reweighting is a procedure commonly used to artificially modify the sample composition so as to obtain a distribution which is more similar to the population. In this paper, we will use post-stratification of the official data, in order to derive the weights necessary for reweighting the sample results, using age and gender as post-stratification variables, thus obtaining more reliable estimation of prevalence and lethality. Specifically, for Italy, we obtain a prevalence of 9%. The proposed methodology represents a reasonable approximation while waiting for more reliable data obtained with a properly designed national sample survey and that it could be further improved if more data were made available.

The role of seasonality in the spread of COVID-19 pandemic

It has been reported that the transmission of COVID-19 can be influenced by the variation of environmental factors due to the seasonal cycle. However, its underlying mechanism in the current and onward transmission pattern remains unclear owing to the limited data and difficulties in separating the impacts of social distancing. Understanding the role of seasonality in the spread of the COVID-19 pandemic is imperative in formulating public health interventions. Here, the seasonal signals of the COVID-19 time series are extracted using the EEMD method, and a modified Susceptible, Exposed, Infectious, Recovered (SEIR) model incorporated with seasonal factors is introduced to quantify its impact on the current COVID-19 pandemic. Seasonal signals decomposed via the EEMD method indicate that infectivity and mortality of SARS-CoV-2 are both higher in colder climates. The quantitative simulation shows that the cold season in the Southern Hemisphere countries caused a 59.71 ± 8.72% increase of the total infections, while the warm season in the Northern Hemisphere countries contributed to a 46.38 ± 29.10% reduction. COVID-19 seasonality is more pronounced at higher latitudes, where larger seasonal amplitudes of environmental indicators are observed. Seasonality alone is not sufficient to curb the virus transmission to an extent that intervention measures are no longer needed, but health care capacity should be scaled up in preparation for new surges in COVID-19 cases in the upcoming cold season. Our study highlights the necessity of considering seasonal factors when formulating intervention strategies.

Temperature dependence of COVID-19 transmission

The recent COVID-19 pandemic follows in its early stages an almost exponential expansion, with the number of cases as a function of time reasonably well fit by N(t) ∝ eαt, in many countries. We analyze the rate α in different countries, starting in each country from a threshold of 30 total cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way. We look for a link between the rate α and the average temperature T of each country, in the month of the initial epidemic growth. We analyze a base set of 42 countries, which developed the epidemic at an earlier stage, an intermediate set of 88 countries and an extended set of 125 countries, which developed the epidemic more recently. Fitting with a linear behavior α(T), we find increasing evidence in the three datasets for a slower spread at high T, at 99.66% C.L., 99.86% C.L. and 99.99995% C.L. (p-value 5⋅10-7, or 5σ detection) in the base, intermediate and extended dataset, respectively. The doubling time at 25 °C is 40% ~ 50% longer than at 5 °C. Moreover we analyzed the possible existence of a bias: poor countries, typically located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita from the dataset, we find that this affects our conclusions only slightly and only for the extended dataset. The significance always remains high, with a p-value of about 10-3 - 10-4 or less. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general, policy measures should be taken to prevent a second wave, such as safe ventilation in public buildings, social distancing, use of masks, testing and tracking policies, before the arrival of the next cold season.

Airborne dispersion of droplets during coughing: a physical model of viral transmission

The Covid-19 pandemic has focused attention on airborne transmission of viruses. Using realistic air flow simulation, we model droplet dispersion from coughing and study the transmission risk related to SARS-CoV-2. Although this model defines most airborne droplets as 8-16 µm in diameter, we infer that larger droplets of 32-40 µm in diameter may potentially be more infectious due to higher viral content. Use of face masks is therefore recommended for both personal and social protection. We found social distancing effective at reducing transmission potential across all droplet sizes. However, the presence of a human body 1 m away modifies the aerodynamics so that downstream droplet dispersion is enhanced, which has implications on safe distancing in queues. At 1 m distance, we found that an average of 0.55 viral copies is inhaled for a cough at median loading, scalable up to 340 copies at peak loading. Droplet evaporation results in significant reduction in droplet counts, but airborne transmission remains possible even under low humidity conditions.

Geospatial analysis of COVID-19 lockdown effects on air quality in the South and Southeast Asian region

The COVID-19 pandemic, induced by the novel Coronavirus worldwide outbreak, is causing countries to introduce different types of lockdown measures to curb the contagion. The implementation of strict lockdown policies has had unprecedented impacts on air quality globally. This study is an attempt to assess the effects of COVID-19 induced lockdown measures on air quality in both regional, country, and city scales in the South and Southeast Asian region using open-source satellite-based data and software frameworks. We performed a systematic review of the national lockdown measures of 19 countries of the study area based on publicly available materials. We considered two temporal settings over a period of 66 days to assess and compare the effects of lockdown measures on air quality levels between standard business as usual and current situation COVID-19 lockdown. Results showed that compared to the same period of 2019, atmospheric NO2, SO2, PM2.5, and CO levels decreased by an average of 24.16%, 19.51%, 20.25%, and 6.88%, respectively during the lockdown, while O3 increased by a maximum of 4.52%. Among the 19 studied cities, Dhaka, Kathmandu, Jakarta, and Hanoi experienced the highest reduction of NO2 (40%-47%) during the lockdown period compared to the corresponding period of 2019. The methodological framework applied in this study can be used and extended to future research in the similar domain such as understanding long-term effects of COVID-19 mitigation measures on the atmospheric pollution at continental-scale or assessing the effects of the domestic emissions during the stay-at-home; a standard and effective COVID-19 lockdown measure applied in most of the countries.

Environmental Indicator for COVID-19 Non-Pharmaceutical Interventions

A novel coronavirus (COVID-19) has caused viral pneumonia worldwide, posing a major threat to international health. Our study reports that city lockdown is an effective way to reduce the number of new cases and the nitrogen dioxide (NO2) concentration can be used as an environmental lockdown indicator to evaluate the effectiveness of lockdown measures. The airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas since the lockdown. The total number of newly confirmed cases reached an inflection point about two weeks since the lockdown and could be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly, and at the same time, the reduction rate of NO2 concentration will increase. Our research results show that NO2 satellite observations can help decision makers effectively monitor and manage non-pharmaceutical interventions in the epidemic.

Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm

COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2^nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R² = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia.

Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy

Italy was the first, among all the European countries, to be strongly hit by the COVID-19 pandemic outbreak caused by the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2). The virus, proven to be very contagious, infected more than 9 million people worldwide (in June 2020). Nevertheless, it is not clear the role of air pollution and meteorological conditions on virus transmission. In this study, we quantitatively assessed how the meteorological and air quality parameters are correlated to the COVID-19 transmission in two large metropolitan areas in Northern Italy as Milan and Florence and in the autonomous province of Trento. Milan, capital of Lombardy region, it is considered the epicenter of the virus outbreak in Italy. Our main findings highlight that temperature and humidity related variables are negatively correlated to the virus transmission, whereas air pollution (PM2.5) shows a positive correlation (at lesser degree). In other words, COVID-19 pandemic transmission prefers dry and cool environmental conditions, as well as polluted air. For those reasons, the virus might easier spread in unfiltered air-conditioned indoor environments. Those results will be supporting decision makers to contain new possible outbreaks.