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Abstract Background Measles is among the most highly infectious human diseases. By virtue of increasingly effective childhood vaccination, together with targeted supplemental immunization activities (SIAs), health authorities in the People’s Republic of China have reduced measles’ reproduction number from about 18 to 2.3. Despite substantial residual susceptibility among young adults, more in some locales than others, sustained routine childhood immunization likely would eliminate measles eventually. To support global eradication efforts, as well as expedite morbidity and mortality reductions in China, we evaluated alternative SIAs via mechanistic mathematical modelling. Methods Our model Chinese population is stratified by immune status (susceptible to measles infection; infected, but not yet infectious; infectious; and recovered or immunized), age (0, 1–4, 5–9, …, 65+ years) and location (31 provinces). Contacts between sub-populations are either empirical or a mixture of preferential and proportionate with respect to age and decline exponentially with distance between locations at age-dependent rates. We estimated initial conditions and most parameters from recent cross-sectional serological surveys, disease surveillance and demographic observations. Then we calculated the reproduction numbers and gradient of the effective number with respect to age- and location-specific immunization rates. We corroborated these analytical results by simulating adolescent and young adult SIAs using a version of our model in which the age-specific contact rates vary seasonally. Results Whereas the gradient indicates that vaccinating young adults generally is the optimal strategy, simulations indicate that a catch-up campaign among susceptible adolescent schoolchildren would accelerate elimination, with timing dependent on uptake. Conclusions These results are largely due to indirect effects (i.e. fewer infections than immunized people might otherwise cause), which meta-population models with realistic mixing are uniquely capable of reproducing accurately.
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Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening
After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R 0 = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial.
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- Award ID(s):
- 2027336
- PAR ID:
- 10319143
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 19
- Issue:
- 188
- ISSN:
- 1742-5662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rsif.2021.0920
