During the COVID-19 pandemic, non-pharmaceutical interventions were employed to limit the spread of the SARS-CoV-2 virus. In some countries, this included large-scale voluntary testing of non-symptomatic individuals to gauge the prevalence in the general population and to identify asymptomatic and pre-symptomatic infections in order to reduce disease spread. Differences between the intensity of testing means that comparing case-counts across borders may be deceptive.In this work, we use mathematical modeling to investigate the dynamics of both observed and unobserved infections as a function of the rate of voluntary testing. The model indicates that increasing the rate of testing causes the number of observed cases to increase, despite a decrease in prevalence due to testing. For large testing rates, the observed case-counts also decrease. The non-monotonicity of the observed cases explains some discrepancies seen when comparing uncorrected case-counts between countries. One such example was observed during the winter 2020/2021 COVID-19 epidemics in Denmark and Hungary, where reported case-counts were comparable, despite differences in prevalence.We determine a quantitative measure for the ascertainment ratio between observed cases and all infections, allowing for test-intensity correction of case-counts. This measure allows us to correct case-counts for testing intensity during the winter 2021/2022 epidemic of the Omicron variant (BA.1 and BA.2) in Denmark, showing good agreement with results from serology-studies.While the model does not capture the full complexity of epidemic dynamics, it provides a simple way to correct case-counts for differences in testing, simplifying comparison across international borders and testing activities.
Heterogeneity, defined as variation in individual-specific traits such as susceptibility or connectivity that are not subject to short-term change, can strongly influence epidemiological dynamics. In particular, it can reduce the effective reproduction number and the herd immunity threshold, thereby increasing the potential to control infectious disease outbreaks. However, heterogeneities at the epidemic level are extremely challenging to observe directly and difficult to estimate indirectly from epidemiological data. This study develops a methodology to estimate heterogeneities in COVID-19 transmission from under-reported incidence data. We estimate the parameters of a modified Susceptible Infected Recovered model using Bayesian Markov Chain Monte Carlo techniques, first testing the proposed method on simulated data to determine its effectiveness before applying it to incompletely reported time-series data from ten Nigerian states. We estimate the average basic reproduction number to be 1.360 (95% credible intervals (CrI): 1.356-1.366), the heterogeneity parameter representing the coefficient of variation in susceptibility to be 2.561 (95% CrI 2.247-2.884), and the reporting probability to be 0.005 (95% CrI 0.004-0.006). Information criteria indicated that the model with heterogeneity was better supported than a homogenous model. The herd immunity threshold with this estimated level of heterogeneity for the best model drops from 0.26 to 0.04 compared to the homogenous model and the percentage of individuals infected in the first wave from over 20% to about 8%. Underestimating the impact of heterogeneities would lead to overestimating the extent of interventions required to bring the effective reproduction number below 1, important when considering the design of control programs that combine different, partially effective interventions.
BACKGROUND:Gains in malaria transmission reduction achieved through insecticide-treated nets (ITNs) are reversing amid emerging artemisinin partial resistance. While sensitive diagnostics are key to monitoring resistant parasite-driven outbreaks, most malaria outbreak models treat diagnostic sensitivity as a fixed background parameter, obscuring its role both in clinical progression and disease burden. METHODS:We developed an eight-compartment deterministic model in which diagnostic sensitivity acts as a bifurcation parameter partitioning individuals infected with uncomplicated malaria into treated and false-negative pathways, incorporating severity-stratified gametocyte production and asymmetric disease-progression rates. The model was validated through local stability and sensitivity analysis. Seven intervention strategies spanning vector control, diagnostics, and treatment allocation were compared via simulations using two complementary outcomes: time for the parasite reservoir to reach ≤ 10% of baseline, and cumulative severe case-days. RESULTS:Raising sensitivity from 0.95 to 0.98 reduced false-negative cases by 60% and mortality by 14%. The top-ranked strategy (90% diagnostic allocation) reached the modeled reservoir threshold 29% faster and 46% cheaper than current ITN-focused practice. Programmatic experiences from Rwanda and Cabo Verde, which have approached or achieved elimination under diagnostic-prioritized frameworks were qualitatively, though not formally, consistent with model predictions. CONCLUSION:Diagnostic sensitivity is a high-leverage, currently underused control point that should precede and inform targeted vector-control deployment and rational antimalarial treatment allocation, offering a lower-cost path toward WHO 2030 elimination targets. Formal model validation against country-level surveillance data and explicit simulation of WHO-recommended antimalarial drug-diversification approaches such as adaptive rotation are identified as priorities for future work.
BACKGROUND:Socioeconomic disparities in COVID-19 outcomes have been widely documented, but evidence regarding inequities in SARS-CoV-2 infection risk remains mixed. In Canada, infection-induced seroprevalence appeared to converge across socioeconomic strata by late 2022, raising questions about whether inequities in infection risk diminished during the Omicron period. AIM:To assess whether apparent convergence in SARS-CoV-2 seroprevalence reflects true equity in infection risk or masks persistent socioeconomic disparities in force of infection. METHODS:We analysed serial cross-sectional SARS-CoV-2 seroprevalence data (anti-nucleocapsid antibodies) from Canadian Blood Services donors collected between April 2021 and April 2023 and stratified by area-level material deprivation quintile (Q1 = least deprived; Q5 = most deprived). We fitted a dynamic seronegative-seropositive model with sero-reversion to the full seroprevalence time series, estimating quintile-specific forces of infection before and after the emergence of the Omicron variant (January 2022). Models allowing differential Omicron-related amplification by material deprivation were compared using likelihood-based criteria. RESULTS:During the pre-Omicron period, the most deprived quintile (Q5) experienced a 71% higher force of infection than the least deprived (Q1; incidence rate ratio (IRR): 1.71; 95% CI: 1.60-1.83). Following Omicron emergence, force of infection rose markedly in all quintiles. Because the pre-Omicron baseline was lowest in the least-deprived quintile, the relative increase during Omicron was largest there (48.5-fold) and smallest in the most-deprived quintile (31.8-fold), compressing the relative socioeconomic gradient (Q5 vs Q1 IRR: 1.12; 95% CI: 1.11-1.14). Despite this compression in relative terms, the most deprived populations continued to experience higher absolute force of infection throughout the Omicron period. CONCLUSION:Convergence in SARS-CoV-2 seroprevalence across socioeconomic strata masked persistent inequalities in force of infection. Dynamic modelling demonstrates that apparent equity is attributable, in our modelling framework, to differential amplification of force of infection during the Omicron period rather than from elimination of underlying socioeconomic disparities.
