Study uses compartmental modeling to test the efficiency of sentinel surveillance for developing public COVID-19 mitigation policies

In a recent study posted to the medRxiv* preprint server, researchers used a stochastic compartmental model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission to examine various sentinel surveillance indicators and determine the minimal sampling effort required for a reliable alert indicating a one-step increase in transmission rate.

Study: Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study. Image Credit: 3DJustincase/Shutterstock
Study: Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study. Image Credit: 3DJustincase/Shutterstock

Background

In the initial stages of the coronavirus disease 2019 (COVID-19) pandemic, before SARS-CoV-2 vaccines were widely available, governments resorted to non-pharmaceutical disease mitigation strategies such as lockdowns and restrictions on businesses to limit the transmission of the virus and ease the burden on the public healthcare system. While timely interventions to “flatten the curve” are necessary, premature enforcement of restrictions could have serious economic repercussions, and delayed responses can cause widespread and harder-to-control infections.

The mitigation policies are often based on commonly-used health indicators such as hospital admissions and the number of reported cases. Hospital admissions often occur two weeks after infection, and reported cases are dependent on diagnostic testing facilities, making these indicators unsuitable for accurate and timely assessment of disease transmission.

Sentinel surveillance includes recorded data on symptom onset, status, and testing date for each recently-symptomatic outpatient case. Sentinel cases can provide a timelier alert to increase transmission rates than hospital admissions as there is a significantly shorter delay in detecting infection, especially for milder cases that do not require hospitalization but can transmit the virus in a population.

About the study

In the present study, the researchers used a stochastic Susceptible Exposed Infected Recovered (SEIR) compartmental model to evaluate the efficiency of surveillance indicators such as hospital admissions, hospital occupancy, and sentinel cases in providing timely, but not premature, alert to a one-step increase in SARS-CoV-2 transmission.

The model included symptom statuses ranging from asymptomatic to severe and considered various disease outcomes such as hospitalization requirement, intensive care unit requirement, and death. Multiple surveillance scenarios were evaluated for each indicator.

Sampling efforts of five, 10, 20, 50, and 100% were considered for sentinel cases to determine the minimal sampling required for a reliable warning of transmission increase. The performance of each indicator was calculated based on how efficient the indicators were in providing a timely alert for mitigative strategies to be enacted.

The cost-benefit analysis for each surveillance indicator was performed using the number of extra days required for disease mitigation and the number of averted deaths in each simulation run. The analysis was also performed for an age-structured SEIR model, with two age groups, one above and one below 40, having different transmission rates and probabilities of symptomatic infections.

Results

The results reported that outpatient sentinel surveillance with a 20% capture of mild cases could report a mild increase in transmission two to five days earlier and a moderate to a severe rise in transmission six days earlier than hospital admission-based indicators. Furthermore, according to the simulations, the sentinel surveillance resulted in fewer false alarms and fewer deaths per day of mitigation.

The study found that hospital admissions were more effective in monitoring transmission rates among the older age group than in the general population since many of the COVID-19-related hospitalizations were older than 40.  

The transmission increase warning was dependent on the age group that first experienced the transmission hike, with alarms being delayed when the transmission increases first occurred in the under 40 years age group. While a 20% sampling of sentinel surveillance cases triggered the alarm six days earlier than hospital admissions when the transmission hike was uniform across age groups, greater than 20% sampling of sentinel surveillance cases was required for a seven-to-nine-day earlier alert when the transmission hike occurred in the under-40 age group first.

The authors believe that one of the challenges in using sentinel surveillance for developing mitigation strategies is achieving the minimum sampling levels, which requires outpatient testing sites to diligently record symptom onset dates and status. With the availability of at-home rapid antigen test kits, the strategy to collect sentinel surveillance data will also have to be reconsidered.

Conclusions

Overall, the results suggested that sentinel surveillance with a 20% sampling of recently-symptomatic cases could provide a reliable and timely alert to increases in COVID-19 transmission rates, despite changing transmission conditions. Developing public health and disease mitigation strategies based on an on-time warning could help avoid widespread infections within a population while circumventing the severe economic consequences of prematurely enforced disease mitigation measures.

*Important notice

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

Journal reference:
Dr. Chinta Sidharthan

Written by

Dr. Chinta Sidharthan

Chinta Sidharthan is a writer based in Bangalore, India. Her academic background is in evolutionary biology and genetics, and she has extensive experience in scientific research, teaching, science writing, and herpetology. Chinta holds a Ph.D. in evolutionary biology from the Indian Institute of Science and is passionate about science education, writing, animals, wildlife, and conservation. For her doctoral research, she explored the origins and diversification of blindsnakes in India, as a part of which she did extensive fieldwork in the jungles of southern India. She has received the Canadian Governor General’s bronze medal and Bangalore University gold medal for academic excellence and published her research in high-impact journals.

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