Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.

Under-reporting of COVID-19 and the limited information about circulating SARS-CoV-2 variants remain major challenges for many African countries. We analyzed SARS-CoV-2 infection dynamics in Addis Ababa and Jimma, Ethiopia, focusing on reinfection, immunity, and vaccination effects. We conducted an antibody serology study spanning August 2020 to July 2022 with five rounds of data collection across a population of 4723, sequenced PCR-test positive samples, used available test positivity rates, and constructed two mathematical models integrating this data. A multivariant model explores variant dynamics identifying wildtype, alpha, delta, and omicron BA.4/5 as key variants in the study population, and cross-immunity between variants, revealing risk reductions between 24% and 69%. An antibody-level model predicts slow decay leading to sustained high antibody levels. Retrospectively, increased early vaccination might have substantially reduced infections during the delta and omicron waves in the considered group of individuals, though further vaccination now seems less impactful.

Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions.

Waddington's landscape provides a conceptual model for developmental processes. It is the basis of various mathematical models describing cell maturation and development at cell and population levels. Yet, these mathematical models mostly disregard cell-to-cell communication, an essential process that modulates cellular decision-making and population dynamics. In this study, we provide a dynamical model for cell maturation and development which can be seen as an extension of Waddington's landscape. The coupled system of partial and ordinary differential equations describes cell density along the cell state together with ligand concentrations. Cell-state-dependent ligand production determines ligand availability, which controls population-level processes. We provide proof of the existence and uniqueness of solutions for our coupled differential equation system and demonstrate the model's validity by analyzing single-cell transcriptomics data. Our results show that cell-to-cell communication is essential for accurately depicting biological recovery processes, such as the regeneration of stem cells in the intestine's crypt and the response of immune cells upon LSP stimulation. Our findings underscore the importance of incorporating cell-to-cell communication into mathematical models of biological development. By doing so, we unlock the potential for deeper insights into complex processes such as tissue regeneration and immune responses, offering new avenues for understanding and predicting the dynamics of biological recovery and cell activation.

This study aimed to retrospectively assess the cost-effectiveness of various COVID-19 vaccination strategies in Ethiopia. It involved healthcare workers (HCWs) and community participants; and was conducted through interviews and serological tests. Local SARS-CoV-2 variants and seroprevalence rates, as well as national COVID-19 reports and vaccination status were also analyzed. A cost-effectiveness analysis was performed to determine the most economical vaccination strategies in settings with limited vaccine access and high SARS-CoV-2 seroprevalence. Before the arrival of the vaccines, 65% of HCWs had antibodies against SARS-CoV-2, indicating prior exposure to the virus. Individuals with prior infection exhibited a greater antibody response to COVID-19 vaccines and experienced fewer new infections compared to those without prior infection, regardless of vaccination status (5% vs. 24%, p < 0.001 for vaccinated; 3% vs. 48%, p < 0.001 for unvaccinated). The cost-effectiveness analysis indicated that a single-dose vaccination strategy is optimal in settings with high underlying seroprevalence and limited vaccine availability. This study underscores the need for pragmatic vaccination strategies tailored to local contexts, particularly in high-seroprevalence regions, to maximize vaccine impact and minimize the spread of COVID-19. Implementing a targeted approach based on local seroprevalence information could have helped Ethiopia achieve higher vaccination rates and prevent subsequent outbreaks.

Under-reporting of COVID-19 cases and the lack of information about circulating SARS-CoV-2 variants remain major challenges for many African countries to date. To address this, we present a comprehensive analysis of the dynamics of SARS-CoV-2 infections in Ethiopia, focusing on reinfection dynamics, (variant-specific) immunity, and the impact of vaccination rates. We conducted an antibody serology study, sequenced positive polymerase chain reaction tests, used available test positivity rates, and constructed two mathematical models integrating these data sets. A multivariant model was used to explore the variant dynamics and differential susceptibility to reinfection based on genetic distances between variants, identifying wildtype, alpha, delta, and omicron BA.4/5 as the four most important variants in Ethiopia. Cross-immunity between variants is investigated, revealing immunities ranging from 24% to 69% risk reduction. A second, antibody-level focused model, predicts slow antibody decay, particularly for Anti-S antibodies, leading to sustained high antibody levels until present times. Retrospectively, it suggests that increased early vaccination could have substantially reduced infections during the delta and omicron waves. However, as a large proportion of the population might have already had multiple infections that led to a strong immune response, further vaccination is less likely to have a significant impact now. *The authors would like to note that Simon Merkt, Solomon Ali, Esayas Kebede Gudina, Jan Hasenauer, Arne Kroidl, and Andreas Wieser respectively contributed equally.