Early waves of the SARS-CoV-2 pandemic were driven by importation events and subsequent policy responses. However, epidemic dynamics in 2021 are largely driven by the spread of more transmissible and/or immune-evading variants, which in turn are countered by vaccination programs. Here we describe updates to the methodology of Covasim (COVID-19 Agent-based Simulator) to account for immune trajectories over time, correlates of protection, co-circulation of different variants and the roll-out of multiple vaccines. We have extended recent work on neutralizing antibodies (NAbs) as a correlate of protection to account for protection against infection, symptomatic COVID-19, and severe disease using a joint estimation approach. We find that NAbs are strongly correlated with infection blocking and that natural infection provides stronger protection than vaccination for the same level of NAbs, though vaccines typically produce higher NAbs. We find only relatively weak correlations between NAbs and the probability of developing symptoms given a breakthrough infection, or the probability of severe disease given symptoms. A more refined understanding of breakthrough infections in individuals with natural and vaccine-derived immunity will have implications for timing of booster vaccines, the impact of emerging variants of concern on critical vaccination thresholds, and the need for ongoing non-pharmaceutical interventions.

Background Estimating the distribution of new HIV infections according to identifiable characteristics is a priority for programmatic planning in HIV prevention. We propose a mathematical modelling approach that uses robust data sources to estimate the distribution of new infections acquired in the generalised epidemics of sub-Saharan Africa and validate it against cohort data. Methods We developed a predictive model that represents the population according to factors powerfully associated with risk: gender, marital status, geographic location, key risk behaviours (sex-work, injecting drug-use, male-to-male sex), sero-discordancy within couples, circumcision and ART status. Incidence inference methods are applied to estimate the short-term distribution of new infections by group. The model is applied within a Bayesian framework whereby regional demographic and epidemiological prior information is updated, where possible, with local data. We validated and trained the model against cohort data from Manicaland (Zimbabwe), Kisesa (Tanzania) and Rakai (Uganda). Building on the results from the acquisition model we infer likely sources of transmission. The model was applied to six countries in the region to investigate potential differences in incidence patterns. Results Without training using the site-specific data, the model was able to predict the pattern of new infections with reasonable accuracy: 95% credible intervals were substantially overlapping and the rank ordering of groups with new infections was consistent. With training using group-specific data on new infections, the accuracy of predictions for subsequent rounds of data improved further and credible intervals narrowed. When applied to the six countries in the region the model showed variation in the distribution of infections between and within countries consistent with the data on prevalence. Conclusions It is possible to accurately predict, the distribution of new HIV infections acquired using data routinely available in many countries in the Sub-Saharan African region. This validated tool can complement additional analyses on resource allocation and data collection priorities.

ObjectivesTo assess the risk of sustained community transmission of SARS-CoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus.DesignWe used an agent-based model Covasim and the demographics, policies, and interventions implemented in the state. Using the calibrated model we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period of zero community transmission.SettingModel calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021.Main outcomesA calibrated model of COVID-19 epidemiology in Queensland; the conditions that could lead to an outbreak; and how likely that situation is to occur.ResultsSimulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying a more infectious variant (e.g., B.1.1.7) has a 43% chance of crossing the same threshold; a threefold increase. Doubling the average number of daily tests results in a decrease of this probability from 43% to 23%.ConclusionsThe introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland.Summary BoxThe knownThe B.1.1.7 variant that emerged in the UK spreads faster than the ancestral COVID-19 strain of early 2020, with a reported transmissibility between 40%-90% higher. However, the probabilities of developing sustained community transmission in Queensland, which is currently a zero community transmission setting, are unknown.The newUsing an agent-based model, with the levels of testing observed in Queensland during February–March 2021, we found that as few as 3 agents infected with a highly-transmissible variant have an 80% chance of developing sustained community transmission.The implicationsUntil high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high adherence rates, will be required to minimise the probability of sustained community transmission from high-transmission variants.