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Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset
Adam Kucharski1, Harriet Mills2, Amy Pinsent2
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Abstract:
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%-32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
Insights
New models estimate human-to-human transmission for emerging pathogens like influenza A/H7N9. This helps prioritize resources by quantifying transmission risk and detecting changes in spread dynamics.
Area of Science:
- Epidemiology
- Infectious Disease Modeling
- Public Health
Background:
- Emerging infectious diseases like MERS-CoV, influenza A/H5N1, and influenza A/H7N9 pose threats through sporadic human cases originating from animal reservoirs.
- Current estimates suggest a basic reproductive number (R0) below or near one for these pathogens, limiting epidemic potential but necessitating precise measurement for resource allocation.
- Accurate, cluster-based R0 estimates are crucial for prioritizing interventions and monitoring transmission dynamics over time.
Purpose of the Study:
- To develop and validate an inferential transmission model to differentiate human-to-human transmission from direct spillover events.
- To provide robust estimates of R0 for low-transmission scenarios, aiding in resource prioritization for emerging infectious diseases.
- To apply the model to real-world outbreak data to assess human transmission contributions and R0 values.
Main Methods:
- Development of an inferential transmission model to distinguish human-to-human spread from zoonotic spillover.
- Simulation studies to assess the model's ability to provide unbiased R0 estimates under varying spillover exposure trends.
- Application of the model to influenza A/H7N9 outbreak data from China in 2013, utilizing known serial intervals and spillover event timing.
Main Results:
- The developed model accurately estimated R0 in simulations, even with unknown temporal spillover trends.
- Analysis of 2013 influenza A/H7N9 outbreaks indicated that human-to-human transmission accounted for 13% of cases.
- Estimated R0 values for the studied clusters were 0.19 (Shanghai), 0.29 (Jiangsu), and 0.03 (Zhejiang).
Conclusions:
- The model effectively quantifies human-to-human transmission and estimates sub-critical R0 values, even with limited data.
- Accurate R0 estimation can inform public health strategies for emerging infectious diseases.
- The methodology offers a tool for real-time monitoring of transmission, crucial for detecting sustained human spread (R0>1).
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