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.

An agent-based model with antibody dynamics information in COVID-19 epidemic simulation

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control.In order to accomplish this objective,we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual,thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points.Our model also permits the evaluation of diverse prevention and control measures.Based on our findings,the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain;however,they may prove inadequate against highly transmissible and less virulent variants.Additionally,our model excels in its ability to trace back to the initial infected case(patient zero)through early epidemic patterns.Ultimately,our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.