New method for estimating the ability of SARS-CoV-2 to spread in a population

Coronavirus disease 2019 (COVID-19) has been the primary target of most epidemiologists and biostatisticians over the past two years, and dozens of articles of varying quality have been published on new methods of modeling the spread of disease, the likelihood of an outbreak occurring and the current threat posed by the disease. Researchers at Curtin University have formulated a new modeling framework to help identify transmissibility of COVID-19 based on periods of varying incidence of cases.

To study: Estimation of transmissibility of SARS-CoV-2 during periods of high, low and no incidence. Image Credit: bob boz / Shutterstock

A pre-printed version of the study is available on the website medRxiv* server while the article is subject to peer review.

The study

The actual number of reproductions depends on the number of contacts an infectious person makes and the likelihood that that contact will infect a second person. Many countries already offer advice on how to limit both of these factors, such as social distancing, hand washing, and avoiding crowded places.

Researchers have identified new techniques to estimate their effectiveness. By observing changes in the rate of social contact and the likelihood of contact infection and examining how these alter the rates of transmission of the virus, they aim to explore the effectiveness of behavior changes in curbing the number of reproductions. .

To estimate the ability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to spread in a population over time, they used a new semi-mechanical model, drawing data from cases, behaviors of the population and the efficiency of the local health system. Transmission is modeled separately for local to local transmission and cases imported from overseas. Local-to-local transmission is modeled using the mean population-level trend induced by interventions that primarily target local-to-local transmission by police, distancing behaviors and isolation of infected individuals, as well as short-term fluctuations in local to local transmission.

This information allows researchers to capture stochastic transmission dynamics, including clusters of cases and times of weaker-than-expected transmission, and assess the ability of the virus to spread during those times.

To estimate the transmission potential or PT, scientists use three sub-models. Physical distancing behaviors fall into two categories: macrodistancing, reducing the average rate of out-of-household contacts, and microdistancing, reducing the probability of transmission through out-of-household contact. Both are assessed through weekly surveys, the data of which can be used to infer temporal trends in behavior. Data on the number of days between symptom onset and case notification can be used to estimate the proportion of cases detected and the time required to notify those infected.

The researchers focused on a period from March 2020 to January 2021 in Australia to demonstrate the interest of their method. They estimated that the potential for transmission declined dramatically across the country until the second half of March 2020, to just under one, following an increase in macro and micro-distance behaviors. The local-to-local transmission potential remained below one in April before increasing steadily in the summer months when restrictions were dismantled.

In New South Wales, a series of outbreaks localized between June and October 2020 have been controlled with specific restrictions. The transmission potential hovered above one, suggesting that these levels of control were insufficient to protect the general population. In November 2020, South Australia suffered a cluster of more than 20 cases due to a violation of the mandatory quarantine order. Despite this, in the previous days, transmission of cases remained low. Scientists estimated the potential for transmission at 1.71 on the day in question, suggesting that an outbreak could occur very quickly once an outbreak is established.

Conclusion

This model could help inform public health decision makers, healthcare workers and epidemiologists, enabling them to make the best decision available to them. Estimating the likelihood of the disease spreading helps identify the most effective social distancing policies, can help hospitals identify the number of beds that are likely to be occupied, and can provide additional information about the disease. This study should make it possible to better understand the epidemic dynamics, to take into account the variability of the types of contacts established and to support the assessment of the situation and the planning of safe reopening.

*Important Notice

medRxiv publishes preliminary scientific reports which are not peer reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or treated as established information


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