ABSTRACT
ObjectivesIn December 2020, an unprecedented vaccination programme to deal with the COVID-19 pandemic was initiated worldwide. However, the vaccine provision is currently insufficient for most countries to vaccinate their entire eligible population, so it is essential to develop the most efficient vaccination strategies. COVID-19 disease severity and mortality vary by age, therefore age-dependent vaccination strategies must be developed. Study design/MethodsHere, we use an age-dependent SIERS (susceptible-infected-exposed-recovered-susceptible) deterministic model to compare four hypothetical age-dependent vaccination strategies and their potential impact on the COVID-19 epidemic in Kyrgyzstan. ResultsOver the short-term (until March 2022), a vaccination rollout strategy focussed on high-risk groups (aged >50 years) with some vaccination among high-incidence groups (aged 20-49 years) may decrease symptomatic cases and COVID-19-attributable deaths. However, there will be limited impact on the estimated overall number of COVID-19 cases with the relatively low coverage of high-incidence groups (15-25% based on current vaccine availability). Vaccination plus non-pharmaceutical interventions (NPIs), such as mask wearing and social distancing, will further decrease COVID-19 incidence and mortality and may have an indirect impact on all-cause mortality. ConclusionsOur results and other evidence suggest that vaccination is most effective in flattening the epidemic curve and reducing mortality if supported by NPIs. In the short-term, focussing on high-risk groups may reduce the burden on the health system and result in fewer deaths. However, the herd effect from delaying another peak may only be achieved by greater vaccination coverage in high-incidence groups.
ABSTRACT
Kyrgyzstan was placed under a two-month, nationwide lockdown due to the COVID-19 epidemic, starting on March 25, 2020. Given the highly disruptive effects of the lockdown on the national economy and peoples lives, the government decided not to extend lockdown beyond the initially planned date of May 10, 2020. The strategy chosen by the government was close to the input parameters of our models baseline scenario, full lockdown release, which we presented to policymakers in April 2020, along with various other hypothetical scenarios with managed lockdown release options. To explore whether our model could accurately predict the actual course of the epidemic following the release of lockdown, we compared the outputs of the baseline scenario, such as new cases, deaths, and demand for and occupancy of hospital beds, with actual official reports. Our analysis revealed that the model could accurately predict the timing of the epidemic peak, with a difference of just two weeks, although the magnitude of the peak was overestimated compared with the official statistics. However, it is important to note that the accuracy of the official reports remains debatable, so outputs relating to the size of the epidemic and related pressures on the health system will need to be updated if new evidence becomes available.
