Efficacy thresholds and target populations for antiviral COVID-19 treatments to save lives and costs: a modelling study.

Background: In 2023 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared endemic, yet hospital admissions have persisted and risen within populations at high and moderate risk of developing severe disease, which include those of older age, and those with co-morbidities. Antiviral treatments, currently only available for high-risk individuals, play an important role in preventing severe disease and hospitalisation within this subpopulation. Here, we further explore the public health and economic benefits of extending target populations for treatment, and assess efficacy thresholds for a treatment strategy to be cost-saving. Methods: We adapted an individual-based transmission model of SARS-CoV-2, OpenCOVID, which was calibrated and validated to 2020-2023 Swiss, European, and Northern Hemisphere epidemiological data. We used the model to estimate hospitalisations and overall costs for preventatively treating three risk groups for a full range of treatment efficacies and coverages with, besides vaccination and hospital treatments, no other interventions in place. We further calculated efficacy thresholds for strategies to be cost-saving. A global sensitivity analysis was conducted to test the sensitivity of all outcomes for a wide range of treatment properties, emerging variant properties, and vaccination coverages. Findings: In a high vaccination coverage setting, we found that a high efficacy antiviral treatment given to all those at high-risk could reduce hospitalisations by up to 40%. When expanding treatment coverage to also include all those at moderate-risk, an additional 50% of hospitalisations could be averted. Targeting both high-risk and moderate-risk groups was found to be cost-saving for a treatment efficacy greater than ∼40%. This threshold was found to be robust regardless of vaccination coverage and emerging variant properties, but highly sensitive to treatment costs. Interpretation: For a sufficiently efficacious antiviral treatment, expanding the target population to include both high-risk and moderate-risk groups should be considered. Equitable treatment costs are found crucial in achieving the best possible public health and health economic outcomes. Funding: Botnar Research Centre for Child Health (DZX2165 to MAP), the Swiss National Science Foundation Professorship of MAP (P00P3_203450) and Swiss National Science Foundation NFP 78 Covid-19 2020 (4079P0_198428 to MAP).

Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control.

Individual-based models of infectious disease dynamics commonly use network structures to represent human interactions. Network structures can vary in complexity, from single-layered with homogeneous mixing to multi-layered with clustering and layer-specific contact weights. Here we assessed policy-relevant consequences of network choice by simulating different network structures within an established individual-based model of SARS-CoV-2 dynamics. We determined the clustering coefficient of each network structure and compared this to several epidemiological outcomes, such as cumulative and peak infections. High-clustered networks estimate fewer cumulative infections and peak infections than less-clustered networks when transmission probabilities are equal. However, by altering transmission probabilities, we find that high-clustered networks can essentially recover the dynamics of low-clustered networks. We further assessed the effect of workplace closures as a layer-targeted intervention on epidemiological outcomes and found in this scenario a single-layered network provides a sufficient approximation of intervention effect relative to a multi-layered network when layer-specific contact weightings are equal. Overall, network structure choice within models should consider the knowledge of contact weights in different environments and pathogen mode of transmission to avoid over- or under-estimating disease burden and impact of interventions.

Seasonal malaria chemoprevention and the spread of Plasmodium falciparum quintuple-mutant parasites resistant to sulfadoxine-pyrimethamine: a modelling study.

BACKGROUND: Seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine plus amodiaquine prevents millions of clinical malaria cases in children younger than 5 years in Africa's Sahel region. However, Plasmodium falciparum parasites partially resistant to sulfadoxine-pyrimethamine (with quintuple mutations) potentially threaten the protective effectiveness of SMC. We evaluated the spread of quintuple-mutant parasites and the clinical consequences. METHODS: We used an individual-based malaria transmission model with explicit parasite dynamics and drug pharmacological models to identify and quantify the influence of factors driving quintuple-mutant spread and predict the time needed for the mutant to spread from 1% to 50% of inoculations for several SMC deployment strategies. We estimated the impact of this spread on SMC effectiveness against clinical malaria. FINDINGS: Higher transmission intensity, SMC coverage, and expanded age range of chemoprevention promoted mutant spread. When SMC was implemented in a high-transmission setting (40% parasite prevalence in children aged 2-10 years) with four monthly cycles to children aged 3 months to 5 years (with 95% initial coverage declining each cycle), the quintuple mutant required 53·1 years (95% CI 50·5-56·0) to spread from 1% to 50% of inoculations. This time increased in lower-transmission settings and reduced by half when SMC was extended to children aged 3 months to 10 years, or reduced by 10-13 years when an additional monthly cycle of SMC was deployed. For the same setting, the effective reduction in clinical cases in children receiving SMC was 79·0% (95% CI 77·8-80·8) and 60·4% (58·6-62·3) during the months of SMC implementation when the quintuple mutant was absent or fixed in the population, respectively. INTERPRETATION: SMC with sulfadoxine-pyrimethamine plus amodiaquine leads to a relatively slow spread of sulfadoxine-pyrimethamine-resistant quintuple mutants and remains effective at preventing clinical malaria despite the mutant spread. SMC with sulfadoxine-pyrimethamine plus amodiaquine should be considered in seasonal settings where this mutant is already prevalent. FUNDING: Swiss National Science Foundation and Marie Curie Individual Fellowship.

Severe outcomes of malaria in children under time-varying exposure.

In malaria epidemiology, interpolation frameworks based on available observations are critical for policy decisions and interpreting disease burden. Updating our understanding of the empirical evidence across different populations, settings, and timeframes is crucial to improving inference for supporting public health. Here, via individual-based modeling, we evaluate a large, multicountry, contemporary Plasmodium falciparum severe malaria dataset to better understand the relationship between prevalence and incidence of malaria pediatric hospitalizations - a proxy of malaria severe outcomes- in East-Africa. We find that life-long exposure dynamics, and subsequent protection patterns in children, substantially determine the likelihood of malaria hospitalizations relative to ongoing prevalence at the population level. Unsteady transmission patterns over a lifetime in children -increasing or decreasing- lead to an exponential relationship of hospitalization rates versus prevalence rather than the asymptotic pattern observed under steady transmission. Addressing this increase in the complexity of malaria epidemiology is crucial to update burden assessments via inference models that guide current and future policy decisions.

Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study.

BACKGROUND: Seasonal malaria chemoprevention (SMC) is recommended for disease control in settings with moderate to high Plasmodium falciparum transmission and currently depends on the administration of sulfadoxine-pyrimethamine plus amodiaquine. However, poor regimen adherence and the increased frequency of parasite mutations conferring sulfadoxine-pyrimethamine resistance might threaten the effectiveness of SMC. Guidance is needed to de-risk the development of drug compounds for malaria prevention. We aimed to provide guidance for the early prioritisation of new and alternative SMC drugs and their target product profiles. METHODS: In this modelling study, we combined an individual-based malaria transmission model that has explicit parasite growth with drug pharmacokinetic and pharmacodynamic models. We modelled SMC drug attributes for several possible modes of action, linked to their potential public health impact. Global sensitivity analyses identified trade-offs between drug elimination half-life, maximum parasite killing effect, and SMC coverage, and optimisation identified minimum requirements to maximise malaria burden reductions. FINDINGS: Model predictions show that preventing infection for the entire period between SMC cycles is more important than drug curative efficacy for clinical disease effectiveness outcomes, but similarly important for impact on prevalence. When children younger than 5 years receive four SMC cycles with high levels of coverage (ie, 69% of children receiving all cycles), drug candidates require a duration of protection half-life higher than 23 days (elimination half-life >10 days) to achieve reductions higher than 75% in clinical incidence and severe disease (measured over the intervention period in the target population, compared with no intervention across a range of modelled scenarios). High coverage is crucial to achieve these targets, requiring more than 60% of children to receive all SMC cycles and more than 90% of children to receive at least one cycle regardless of the protection duration of the drug. INTERPRETATION: Although efficacy is crucial for malaria prevalence reductions, chemoprevention development should select drug candidates for their duration of protection to maximise burden reductions, with the duration half-life determining cycle timing. Explicitly designing or selecting drug properties to increase community uptake is paramount. FUNDING: Bill & Melinda Gates Foundation and the Swiss National Science Foundation.

A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective.

This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

Repurposing Know-how for Drug Development: Case Studies from the Swiss Tropical and Public Health Institute.

In pursuing novel therapeutic solutions, drug discovery and development rely on efficiently utilising existing knowledge and resources. Repurposing know-how, a strategy that capitalises on previously acquired information and expertise, has emerged as a powerful approach to accelerate drug discovery and development processes, often at a fraction of the costs of de novo developments. For 80 years, collaborating within a network of partnerships, the Swiss Tropical and Public Health Institute (Swiss TPH) has been working along a value chain from innovation to validation and application to combat poverty-related diseases. This article presents an overview of selected know-how repurposing initiatives conducted at Swiss TPH with a particular emphasis on the exploration of drug development pathways in the context of neglected tropical diseases and other infectious diseases of poverty, such as schistosomiasis, malaria and human African trypanosomiasis.

Modelling to inform next-generation medical interventions for malaria prevention and treatment.

Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. Target product profiles and preferred product characteristics play a key role in setting selection criteria requirements and early endorsement by health authorities. While clinical evidence and expert opinion often inform product development decisions, integrating modelling evidence early and iteratively into this process provides an opportunity to link product characteristics with expected public health outcomes. Population models of malaria transmission can provide a better understanding of which, and at what magnitude, key intervention characteristics drive public health impact, and provide quantitative evidence to support selection of use-cases, transmission settings, and deployment strategies. We describe how modelling evidence can guide and accelerate development of new malaria vaccines, monoclonal antibodies, and chemoprevention.

Patient variability in the blood-stage dynamics of Plasmodium falciparum captured by clustering historical data.

BACKGROUND: Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. METHODS: Patients' parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). RESULTS: To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. CONCLUSION: This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.

Accelerated development of malaria monoclonal antibodies.

L9LS, a potent and safe antimalarial monoclonal antibody, demonstrated 88% protective efficacy against infection in a phase 1 trial in healthy adults.1 These promising results are the first of many to usher in a potential new era of malaria prevention.

COVID-19 Vaccine Booster Strategies in Light of Emerging Viral Variants: Frequency, Timing, and Target Groups.

BACKGROUND: Vaccinations have reduced severe burden of COVID-19 and allowed for lifting of non-pharmaceutical interventions. However, with immunity waning alongside emergence of more transmissible variants of concern, vaccination strategies must be examined. METHODS: Here we apply a SARS-CoV-2 transmission model to identify preferred frequency, timing, and target groups for vaccine boosters to reduce public health burden and health systems risk. We estimated new infections and hospital admissions averted over 2 years through annual or biannual boosting of those eligible (those who received doses one and two) who are (1) most vulnerable (60+ or living with comorbidities) or (2) those 5+, at universal (98% of eligible) or lower coverage (85% of those 50+ or with comorbidities and 50% of 5-49 year olds) representing moderate vaccine fatigue and/or hesitancy. We simulated three emerging variant scenarios: (1) no new variants; (2) 25% more infectious and immune-evading Omicron-level severity variants emerge annually and become dominant; (3) emerge biannually. We further explored the impact of varying seasonality, variant immune-evading capacity, infectivity, severity, timing, and vaccine infection blocking assumptions. RESULTS: To reduce COVID-19-related hospitalisations over the next 2 years, boosters should be provided for all those eligible annually 3-4 months ahead of peak winter whether or not new variants of concern emerge. Only boosting those most vulnerable is unlikely to ensure reduced stress on health systems. Moreover, boosting all eligible better protects those most vulnerable than only boosting the vulnerable group. Conversely, while this strategy may not ensure reduced stress on health systems, as an indication of cost-effectiveness, per booster dose more hospitalisations could be averted through annual boosting of those most vulnerable versus all eligible, since those most vulnerable are more likely to seek hospital care once infected, whereas increasing to biannual boosting showed diminishing returns. Results were robust when key model parameters were varied. However, we found that the more frequently variants emerge, the less the effect boosters will have, regardless of whether administered annually or biannually. CONCLUSIONS: Delivering well-timed annual COVID-19 vaccine boosters to all those eligible, prioritising those most vulnerable, can reduce infections and hospital admissions. Findings provide model-based evidence for decision-makers to plan for administering COVID-19 boosters ahead of winter 2022-2023 to help mitigate the health burden and health system stress.

Pre-existing partner-drug resistance to artemisinin combination therapies facilitates the emergence and spread of artemisinin resistance: a consensus modelling study.

BACKGROUND: Artemisinin-resistant genotypes of Plasmodium falciparum have now emerged a minimum of six times on three continents despite recommendations that all artemisinins be deployed as artemisinin combination therapies (ACTs). Widespread resistance to the non-artemisinin partner drugs in ACTs has the potential to limit the clinical and resistance benefits provided by combination therapy. We aimed to model and evaluate the long-term effects of high levels of partner-drug resistance on the early emergence of artemisinin-resistant genotypes. METHODS: Using a consensus modelling approach, we used three individual-based mathematical models of Plasmodium falciparum transmission to evaluate the effects of pre-existing partner-drug resistance and ACT deployment on the evolution of artemisinin resistance. Each model simulates 100 000 individuals in a particular transmission setting (malaria prevalence of 1%, 5%, 10%, or 20%) with a daily time step that updates individuals' infection status, treatment status, immunity, genotype-specific parasite densities, and clinical state. We modelled varying access to antimalarial drugs if febrile (coverage of 20%, 40%, or 60%) with one primary ACT used as first-line therapy: dihydroartemisinin-piperaquine (DHA-PPQ), artesunate-amodiaquine (ASAQ), or artemether-lumefantrine (AL). The primary outcome was time until 0·25 580Y allele frequency for artemisinin resistance (the establishment time). FINDINGS: Higher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance. Across all models, a 10-fold increase in the frequency of partner-drug resistance genotypes on average corresponded to loss of artemisinin efficacy 2-12 years earlier. Most reductions in time to artemisinin resistance establishment were observed after an increase in frequency of the partner-drug resistance genotype from 0·0 to 0·10. INTERPRETATION: Partner-drug resistance in ACTs facilitates the early emergence of artemisinin resistance and is a major public health concern. Higher-grade partner-drug resistance has the largest effect, with piperaquine resistance accelerating the early emergence of artemisinin-resistant alleles the most. Continued investment in molecular surveillance of partner-drug resistant genotypes to guide choice of first-line ACT is paramount. FUNDING: Schmidt Science Fellowship in partnership with the Rhodes Trust; Bill & Melinda Gates Foundation; Wellcome Trust.

Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden.

Background: SARS-CoV-2 variants of concern, such as Omicron (B.1.1.529), continue to emerge. Assessing the impact of their potential viral properties on the probability of future transmission dominance and public health burden is fundamental in guiding ongoing COVID-19 control strategies. Methods: With an individual-based transmission model, OpenCOVID, we simulated three viral properties; infectivity, severity, and immune-evading ability, all relative to the Delta variant, to identify thresholds for Omicron's or any emerging VOC's potential future dominance, impact on public health, and risk to health systems. We further identify for which combinations of viral properties current interventions would be sufficient to control transmission. Results: We show that, with first-generation SARS-CoV-2 vaccines and limited physical distancing in place, a VOC's potential future dominance is primarily driven by its infectivity, which does not always lead to an increased public health burden. However, we also show that highly immune-evading variants that become dominant, even in the case of reduced variant severity, would likely require alternative measures to avoid strain on health systems, such as strengthened physical distancing measures, novel treatments, and second-generation vaccines. Expanded vaccination, that includes a booster dose for adults and child vaccination strategies, is projected to have the biggest public health benefit for a highly infective, highly severe VOC with low immune-evading capacity. Conclusions: These findings provide quantitative guidance to decision-makers at a critical time while Omicron's properties are being assessed and preparedness for emerging VOCs is eminent. We emphasise the importance of both genomic and population epidemiological surveillance.

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum.

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure-treatment properties, biological factors, transmission intensity, and access to treatment-obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.

BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

Population Pharmacokinetics of Antimalarial Naphthoquine in Combination with Artemisinin in Tanzanian Children and Adults: Dose Optimization.

The combination antimalarial therapy of artemisinin-naphthoquine (ART-NQ) was developed as a single-dose therapy, aiming to improve adherence relative to the multiday schedules of other artemisinin combination therapies. The pharmacokinetics of ART-NQ has not been well characterized, especially in children. A pharmacokinetic study was conducted in adults and children over 5 years of age (6 to 10, 11 to 17, and ≥18 years of age) with uncomplicated malaria in Tanzania. The median weights for the three age groups were 20, 37.5, and 55 kg, respectively. Twenty-nine patients received single doses of 20 mg/kg of body weight for artemisinin and 8 mg/kg for naphthoquine, and plasma drug concentrations were assessed at 13 time points over 42 days from treatment. We used nonlinear mixed-effects modeling to interpret the data, and allometric scaling was employed to adjust for the effect of body size. The pharmacokinetics of artemisinin was best described by one-compartment model and that of naphthoquine by a two-compartment disposition model. Clearance values for a typical patient (55-kg body weight and 44.3-kg fat-free mass) were estimated as 66.7 L/h (95% confidence interval [CI], 57.3 to 78.5 L/h) for artemisinin and 44.2 L/h (95% CI, 37.9 to 50.6 L/h) for naphthoquine. Nevertheless, we show via simulation that patients weighing ≥70 kg achieve on average a 30% lower day 7 concentration compared to a 48-kg reference patient at the doses tested, suggesting dose increases may be warranted to ensure adequate exposure. (This study has been registered at ClinicalTrials.gov under identifier NCT01930331.).

The empirical support for the radical cure strategy for eliminating Plasmodium vivax in China.

BACKGROUND: With the recent certification by World Health Organization that the People's Republic of China is malaria-free, it is timely to consider how elimination of malaria was completed in People's Republic of China over the last 7 decades. Of the four widespread species of human malaria, Plasmodium vivax was the last to be eliminated by the national program of China. Understanding the incubation periods and relapses patterns of P. vivax through historical data from China is relevant for planning disease elimination in other malaria-endemic countries, with residual P. vivax malaria. METHODS: We collated data from both published and unpublished malaria parasite inoculation experiments conducted between 1979 and 1988 with parasites from different regions of the People's Republic of China. The studies had at least two years of follow-up. We categorized P. vivax incubation patterns via cluster analysis and investigated relapse studies by adapting a published within-host relapse model for P. vivax temperate phenotypes. Each model was fitted using the expectation-maximization (EM) algorithm initialized by hierarchical model-based agglomerative clustering. RESULTS: P. vivax parasites from the seven studies of five southern and central provinces in the People's Republic of China covering geographies ranging from the south temperate to north tropical zones. The parasites belonged to two distinct phenotypes: short- (10-19 days) or long-incubation (228-371 days). The larger the sporozoite inoculation, the more likely short incubation periods were observed, and with more subsequent relapses (Spearman's rank correlation between the number of inoculated sporozoites and the number of relapses of 0.51, p-value = 0.0043). The median of the posterior distribution for the duration of the first relapse interval after primary infection was 168.5 days (2.5% quantile: 89.7; 97.5% quantile: 227.69 days). The predicted survival proportions from the within-host model fit well to the original relapse data. The within-host model also captures the hypnozoite activation rates and relapse frequencies, which consequently influences the transmission possibility of P. vivax. CONCLUSIONS: Through a within-host model, we demonstrate the importance of clearance of hypnozoites. A strategy of two rounds of radical hypnozoite clearance via mass drug administration (MDA) deployed during transmission (summer and autumn) and non-transmission (late spring) seasons had a pronounced effect on outbreaks during the malaria epidemics in China. This understanding can inform malaria control strategies in other endemic countries with similar settings.

Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention.

Seasonal malaria chemoprevention (SMC) has proven highly efficacious in reducing malaria incidence. However, the continued success of SMC is threatened by the spread of resistance against one of its main preventive ingredients, Sulfadoxine-Pyrimethamine (SP), operational challenges in delivery, and incomplete adherence to the regimens. Via a simulation study with an individual-based model of malaria dynamics, we provide quantitative evidence to assess long-acting injectables (LAIs) as potential alternatives to SMC. We explored the predicted impact of a range of novel preventive LAIs as a seasonal prevention tool in children aged three months to five years old during late-stage clinical trials and at implementation. LAIs were co-administered with a blood-stage clearing drug once at the beginning of the transmission season. We found the establishment of non-inferiority of LAIs to standard 3 or 4 rounds of SMC with SP-amodiaquine was challenging in clinical trial stages due to high intervention deployment coverage. However, our analysis of implementation settings where the achievable SMC coverage was much lower, show LAIs with fewer visits per season are potential suitable replacements to SMC. Suitability as a replacement with higher impact is possible if the duration of protection of LAIs covered the duration of the transmission season. Furthermore, optimising LAIs coverage and protective efficacy half-life via simulation analysis in settings with an SMC coverage of 60% revealed important trade-offs between protective efficacy decay and deployment coverage. Our analysis additionally highlights that for seasonal deployment for LAIs, it will be necessary to investigate the protective efficacy decay as early as possible during clinical development to ensure a well-informed candidate selection process.

Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland.

BACKGROUND: As vaccination coverage against SARS-CoV-2 increases amidst the emergence and spread of more infectious and potentially more deadly viral variants, decisions on timing and extent of relaxing effective, but unsustainable, non-pharmaceutical interventions (NPIs) need to be made. METHODS: An individual-based transmission model of SARS-CoV-2 dynamics, OpenCOVID, was developed to compare the impact of various vaccination and NPI strategies on the COVID-19 epidemic in Switzerland. OpenCOVID uses the Oxford Containment Health Index (OCHI) to quantify the stringency of NPIs. RESULTS: Even if NPIs in place in March 2021 were to be maintained and the vaccine campaigns rollout rapidly scaled-up, a 'third wave' was predicted. However, we find a cautious phased relaxation can substantially reduce population-level morbidity and mortality. We find that a faster vaccination campaign can offset the size of such a wave, allowing more flexibility for NPIs to be relaxed sooner. Model outcomes were most sensitive to the level of infectiousness of variants of concern observed in Switzerland. CONCLUSION: A rapid vaccination rollout can allow the sooner relaxation of NPIs, however ongoing surveillance of - and swift responses to - emerging viral variants is of utmost importance for epidemic control.

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.