An artificially simulated outbreak of a respiratory infectious disease

Dynamic model of respiratory infectious disease transmission in urban public transportation systems

During the epidemics of respiratory infectious diseases, the use of public transportation increases the risk of disease transmission. Therefore, we established a dynamic model to provide an in-depth understanding of the mechanism of epidemic spread via this route. We designed a computer program to model a rail transit system including four transit lines in a small town in which assumed 70% of the residents commute via these trams in weekdays and the remaining residents take the tram at random. The model could identify the best travel route for each passenger and the specific passengers onboard when the tram passed through each station, and simulate the dynamic spread of a respiratory pathogen as the passengers used the rail transit system. Based on the program operating, we estimated that all residents in the town were ultimately infected, including 86.6% who were infected due to the public transportation system. The remaining individuals were infected at home. As the infection rate increased, the number of infected individuals increased more rapidly. Reducing the frequency of trams, driving private cars or riding bicycles, showing nucleic acid certificates and wearing masks for passengers, etc., are effective measures for the prevention of the spread of epidemic diseases.

A dynamic model for elevator operation-induced spread of a respiratory infectious disease in an apartment building

Residents have to use elevators to leave and enter their high-rise apartments frequently. An elevator car can easily spread respiratory infectious diseases, as it has a confined and small space. Therefore, studying how elevator operations promote epidemic transmission is of importance to public health. We developed an infectious disease dynamics model. First, we used homemade codes to simulate the operating state of an elevator and the dynamic process of infectious disease transmission in an apartment building due to elevator operations. Second, we analysed the temporal distribution patterns of infected individuals and patients. Finally, we validated the reliability of the model by performing continuous-time sensitivity analysis on important model parameters. We found that elevator operations can cause rapid spread of infectious diseases within an apartment building. Therefore, it is necessary to enhance elevator ventilation and disinfection mechanisms to prevent the outbreak of respiratory infections. Moreover, residents should reduce elevator use and wear masks.

Simulation of COVID-19 spread through family feast gatherings in a complex network

Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB). Here, we analyzed data on cattle movement and slaughterhouse surveillance for detection of TB-like lesions (TLL) over the 2016-2018 period in the state of Rio Grande do Sul (RS) in Brazil with the following aims: (i) to characterize cattle trade describing the static full, yearly, and monthly snapshots of the network contact trade, (ii) to identify clusters in the space and contact networks of premises from which animals with TLL originated, and (iii) to evaluate the potential of targeted control actions to decrease TB spread in the cattle population of RS using a stochastic metapopulation disease transmission model that simulated within-farm and between-farm disease spread. We found heterogeneous densities of premises and animals in the study area. The analysis of the contact network revealed a highly connected (~94%) trade network, with strong temporal trends, especially for May and November. The TLL cases were significantly clustered in space and in the contact network, suggesting the potential for both local (e.g., fence-to-fence) and movement-mediated TB transmission. According to the disease spread model, removing the top 7% connected farms based on degree and betweenness could reduce the total number of infected farms over three years by >50%. In conclusion, the characterization of the cattle network suggests that highly connected farms may play a role in TB dissemination, although being close to infected farms was also identified as a risk factor for having animals with TLL. Surveillance and control actions based on degree and betweenness could be useful to break the transmission cycle between premises in RS.

Analysis and prediction of the coronavirus disease epidemic in China based on an individual-based model

We established a stochastic individual-based model and simulated the whole process of occurrence, development, and control of the coronavirus disease epidemic and the infectors and patients leaving Hubei Province before the traffic was closed in China. Additionally, the basic reproduction number (R0) and number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R0 at the initial stage of the epidemic was 4.97 (95% confidence interval [CI] 4.82-5.17). Before the traffic lockdown was implemented in Hubei, 2000 (95% CI 1982-2030) infectors and patients had left Hubei and traveled throughout the country. The model estimated that if the government had taken prevention and control measures 1 day later, the cumulative number of laboratory-confirmed patients in the whole country would have increased by 32.1%. If the lockdown of Hubei was imposed 1 day in advance, the cumulative number of laboratory-confirmed patients in other provinces would have decreased by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. The intervention measurements nationwide have effectively curbed the human-to-human transmission of severe acute respiratory syndrome coronavirus 2.

Singapore COVID-19 Pandemic Response as a Successful Model Framework for Low-Resource Health Care Settings in Africa?

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to spread and evolve across the planet. The crosscutting impacts of the virus, individual country responses to the virus, and the state of preparedness of local public health systems greatly vary across the world. The ostensibly late arrival of the virus in Africa has allowed learning, innovation, and adaptation of methods that have been successful in the early-hit countries. This article analyzes how Singapore has responded to the COVID-19 pandemic and proposes that adaptations of the Singapore pandemic response model would bode well for Africa's response to the COVID-19 pandemic in ways that also take into account regional differences in health care infrastructures, socioeconomic resilience, poverty, and the vast population diversity in the African continent. As the pandemic evolves, the lessons learned in Asia, in particular, and the emerging new experiences in African countries should inform, ideally in real time, how best to steer the world populations into safety, including those in low-resource health care settings. Finally, we note that the current COVID-19 pandemic is also a test for our collective ability to scale and surge public health in response to future and likely equally challenging zoonosis infections that jump from animals to humans, not to mention climate change-related planetary health calamities in the 21st century. Hence, what we learn effectively from the current COVID-19 pandemic shall have broad, enduring, and intergenerational relevance for the future of planetary heath and society.