Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts

The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease.

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

Natural Bioactive Molecules: An Alternative Approach to the Treatment and Control of COVID-19

Several coronaviruses (CoVs) have been associated with serious health hazards in recent decades, resulting in the deaths of thousands around the globe. The recent coronavirus pandemic has emphasized the importance of discovering novel and effective antiviral medicines as quickly as possible to prevent more loss of human lives. Positive-sense RNA viruses with group spikes protruding from their surfaces and an abnormally large RNA genome enclose CoVs. CoVs have already been related to a range of respiratory infectious diseases possibly fatal to humans, such as MERS, SARS, and the current COVID-19 outbreak. As a result, effective prevention, treatment, and medications against human coronavirus (HCoV) is urgently needed. In recent years, many natural substances have been discovered with a variety of biological significance, including antiviral properties. Throughout this work, we reviewed a wide range of natural substances that interrupt the life cycles for MERS and SARS, as well as their potential application in the treatment of COVID-19.

Public Health Measures During the COVID-19 Pandemic Reduce the Spread of Other Respiratory Infectious Diseases

Background: Public health measures (such as wearing masks, physical distancing, and isolation) have significantly reduced the spread of the coronavirus disease-2019 (COVID-19), but the impact of public health measures on other respiratory infectious diseases is unclear.Objective: To assess the correlation between public health measures and the incidence of respiratory infectious diseases in China during the COVID-19 pandemic.Methods: We collected the data from the National Health and Construction Commission in China on the number of patients with six respiratory infectious diseases (measles, tuberculosis, pertussis, scarlet fever, influenza, and mumps) from 2017 to 2020 and assessed the correlation between public health measures and the incidence of respiratory infectious diseases. Finally, we used the data of the six respiratory infectious diseases in 2021 to verify our results.Results: We found public health measures significantly reduced the incidence of measles (p = 0.002), tuberculosis (p = 0.002), pertussis (p = 0.004), scarlet fever (p = 0.002), influenza (p = 0.034), and mumps (p = 0.002) in 2020, and prevented seasonal peaks. Moreover, the effects of public health measures were most marked during the peak seasons for these infections. Of the six respiratory infectious diseases considered, tuberculosis was least affected by public health measures.Conclusion: Public health measures were very effective in reducing the incidence of respiratory infectious diseases, especially when the respiratory infectious diseases would normally have been at their peak.

Experimental investigations of application of the water-steam оzone-containing gaseous mixture for prophylaxis and treatment of respiratory infectious diseases

Objective. Determination of the virus-cidal and bacterio-cidal action of the ozone-containing steam-water mixture, which was obtained, using apparatus POS-1, simulated on viral models of transmissive gastroenteritis of pigs of the coronaviruses family and polyresistant clinical strains of bacterial cultures, as well as adjustment of optimal parameters of ozone in the gaseous mixture content, which do not cause pathological changes in the organism’s organs and systems. Materials and methods. Apparatus POS-1, created by collective of Scientific-Methodical Centre «Medical Innovation Technologies»», was applied for production of the ozone-containing gaseous mixture. Virusological investigations were conducted on the base of the Institute of Epidemiology and Infectious Diseases named after L. V, Gromashevskiy. As a working material the models of virus of the pigs transmissive gastroenteritis of the coronaviruses family were applied. The series of bacteriological and experimental investigations were conducted on the base of Shalimov National Institute of Surgery and Transplantology NAMS of Ukraine. Polyresistant cultures in concentration of 108 colony-creating units per 1 ml were applied for bacteriological investigations. The experiment was conducted on white rats to study the gaseous ozone-containing mixture affection on living biological tissues. Results. Virusological investigations have shown, that in the 20 minutes exposition and a contact with extracellular virus the infection titer have lowered in 100 000 times, and in a 30 minutes exposition - a complete deactivation of virus have had occurred. Bacteriological investigations have revealed the complete absence of the cultures development after their processing with the ozone-containing mixture during 20 min. The results of swimming tests and behavioral reactions in white rats of control and experimental groups did not differ. Histological investigations of the respiratory organs tissues as well as of spleen, thyroid gland, kidneys and suprarenal glands did not reveal pathological effects of the ozone-containing mixture. Conclusion. Parameters of the ozone generation and delivery in the gaseous mixture content were studied and optimally selected. Investigations of the ozone-containing mixture affection on a viral strain of the coronaviruses family, as well as bacteriological investigations on polyresistant bacterial cultures have had confirmed its pronounced virus-cidal and bactericidal properties. Application of the ozone-containing mixture do not cause any pathological changes in the living organism organs and systems. This gaseous mixture may be used for prevention and treatment of respiratory infectious diseases of viral and bacterial genesis.

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.