Establishment of an African green monkey model for COVID-19 and protection against re-infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for an unprecedented global pandemic of COVID-19. Animal models are urgently needed to study the pathogenesis of COVID-19 and to screen vaccines and treatments. We show that African green monkeys (AGMs) support robust SARS-CoV-2 replication and develop pronounced respiratory disease, which may more accurately reflect human COVID-19 cases than other nonhuman primate species. SARS-CoV-2 was detected in mucosal samples, including rectal swabs, as late as 15 days after exposure. Marked inflammation and coagulopathy in blood and tissues were prominent features. Transcriptome analysis demonstrated stimulation of interferon and interleukin-6 pathways in bronchoalveolar lavage samples and repression of natural killer cell- and T cell-associated transcripts in peripheral blood. Despite a slight waning in antibody titers after primary challenge, enhanced antibody and cellular responses contributed to rapid clearance after re-challenge with an identical strain. These data support the utility of AGM for studying COVID-19 pathogenesis and testing medical countermeasures.

Meningococcal ACWYX Conjugate Vaccine in 2-to-29-Year-Olds in Mali and Gambia.

BACKGROUND: An effective, affordable, multivalent meningococcal conjugate vaccine is needed to prevent epidemic meningitis in the African meningitis belt. Data on the safety and immunogenicity of NmCV-5, a pentavalent vaccine targeting the A, C, W, Y, and X serogroups, have been limited. METHODS: We conducted a phase 3, noninferiority trial involving healthy 2-to-29-year-olds in Mali and Gambia. Participants were randomly assigned in a 2:1 ratio to receive a single intramuscular dose of NmCV-5 or the quadrivalent vaccine MenACWY-D. Immunogenicity was assessed at day 28. The noninferiority of NmCV-5 to MenACWY-D was assessed on the basis of the difference in the percentage of participants with a seroresponse (defined as prespecified changes in titer; margin, lower limit of the 96% confidence interval [CI] above -10 percentage points) or geometric mean titer (GMT) ratios (margin, lower limit of the 98.98% CI >0.5). Serogroup X responses in the NmCV-5 group were compared with the lowest response among the MenACWY-D serogroups. Safety was also assessed. RESULTS: A total of 1800 participants received NmCV-5 or MenACWY-D. In the NmCV-5 group, the percentage of participants with a seroresponse ranged from 70.5% (95% CI, 67.8 to 73.2) for serogroup A to 98.5% (95% CI, 97.6 to 99.2) for serogroup W; the percentage with a serogroup X response was 97.2% (95% CI, 96.0 to 98.1). The overall difference between the two vaccines in seroresponse for the four shared serogroups ranged from 1.2 percentage points (96% CI, -0.3 to 3.1) for serogroup W to 20.5 percentage points (96% CI, 15.4 to 25.6) for serogroup A. The overall GMT ratios for the four shared serogroups ranged from 1.7 (98.98% CI, 1.5 to 1.9) for serogroup A to 2.8 (98.98% CI, 2.3 to 3.5) for serogroup C. The serogroup X component of the NmCV-5 vaccine generated seroresponses and GMTs that met the prespecified noninferiority criteria. The incidence of systemic adverse events was similar in the two groups (11.1% in the NmCV-5 group and 9.2% in the MenACWY-D group). CONCLUSIONS: For all four serotypes in common with the MenACWY-D vaccine, the NmCV-5 vaccine elicited immune responses that were noninferior to those elicited by the MenACWY-D vaccine. NmCV-5 also elicited immune responses to serogroup X. No safety concerns were evident. (Funded by the U.K. Foreign, Commonwealth, and Development Office and others; ClinicalTrials.gov number, NCT03964012.).

Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

A Physics-Informed Neural Network approach for compartmental epidemiological models.

Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their calibration is not straightforward, since many factors contribute to the rapid change of the transmission dynamics. For example, there might be changes in the individual awareness, the imposition of non-pharmacological interventions and the emergence of new variants. As a consequence, model parameters such as the transmission rate are doomed to vary in time, making their assessment more challenging. Here, we propose to use Physics-Informed Neural Networks (PINNs) to track the temporal changes in the model parameters and the state variables. PINNs recently gained attention in many engineering applications thanks to their ability to consider both the information from data (typically uncertain) and the governing equations of the system. The ability of PINNs to identify unknown model parameters makes them particularly suitable to solve ill-posed inverse problems, such as those arising in the application of epidemiological models. Here, we develop a reduced-split approach for the implementation of PINNs to estimate the temporal changes in the state variables and transmission rate of an epidemic based on the SIR model equation and infectious data. The main idea is to split the training first on the epidemiological data, and then on the residual of the system equations. The proposed method is applied to five synthetic test cases and two real scenarios reproducing the first months of the Italian COVID-19 pandemic. Our results show that the split implementation of PINNs outperforms the joint approach in terms of accuracy (up to one order of magnitude) and computational times (speed up of 20%). Finally, we illustrate that the proposed PINN-method can also be adopted to produced short-term forecasts of the dynamics of an epidemic.

When should lockdown be implemented? Devising cost-effective strategies for managing epidemics amid vaccine uncertainty.

During an infectious disease outbreak, public health policy makers are tasked with strategically implementing interventions whilst balancing competing objectives. To provide a quantitative framework that can be used to guide these decisions, it is helpful to devise a clear and specific objective function that can be evaluated to determine the optimal outbreak response. In this study, we have developed a mathematical modelling framework representing outbreaks of a novel emerging pathogen for which non-pharmaceutical interventions (NPIs) are imposed or removed based on thresholds for hospital occupancy. These thresholds are set at different levels to define four unique strategies for disease control. We illustrate that the optimal intervention strategy is contingent on the choice of objective function. Specifically, the optimal strategy depends on the extent to which policy makers prioritise reducing health costs due to infection over the costs associated with maintaining interventions. Motivated by the scenario early in the COVID-19 pandemic, we incorporate the development of a vaccine into our modelling framework and demonstrate that a policy maker's belief about when a vaccine will become available in future, and its eventual coverage (and/or effectiveness), affects the optimal strategy to adopt early in the outbreak. Furthermore, we show how uncertainty in these quantities can be accounted for when deciding which interventions to introduce. This research highlights the benefits of policy makers being explicit about the precise objectives of introducing interventions.

The influence of cross-border mobility on the COVID-19 epidemic in Nordic countries.

Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that the effect of inter-country mobility on epidemic growth is non-negligible essentially when there is sizeable mobility from a high prevalence country or countries to a low prevalence one. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions.

Understanding the coevolution of mask wearing and epidemics: A network perspective.

Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of "beneficial contagions," e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease-behavior dynamics, the attack rate of a disease-as a function of transition probability-exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size.

Long-term benefits of nonpharmaceutical interventions for endemic infections are shaped by respiratory pathogen dynamics.

COVID-19 nonpharmaceutical interventions (NPIs), including mask wearing, have proved highly effective at reducing the transmission of endemic infections. A key public health question is whether NPIs could continue to be implemented long term to reduce the ongoing burden from endemic pathogens. Here, we use epidemiological models to explore the impact of long-term NPIs on the dynamics of endemic infections. We find that the introduction of NPIs leads to a strong initial reduction in incidence, but this effect is transient: As susceptibility increases, epidemics return while NPIs are in place. For low R0 infections, these return epidemics are of reduced equilibrium incidence and epidemic peak size. For high R0 infections, return epidemics are of similar magnitude to pre-NPI outbreaks. Our results underline that managing ongoing susceptible buildup, e.g., with vaccination, remains an important long-term goal.

HIV Care Continuum and Preexposure Prophylaxis Program in Federal Bureau of Prisons, United States.

In 2019, the US Department of Health and Human Services launched the Ending the HIV Epidemic in the US initiative (EHE) with the goal of reducing new HIV infections by 90% by 2030. This initiative identifies 4 pillars (diagnose, treat, prevent, and respond) to address the HIV epidemic in the United States. To advance the EHE goals, the Federal Bureau of Prisons (FBOP) has implemented interventions at all points of the HIV care continuum. The FBOP has addressed the EHE pillar of prevention through implementing preexposure prophylaxis, developing a strategy to decrease the risk of new HIV infection, and providing guidance to FBOP healthcare providers. This article describes the implementation of programs to improve the HIV care continuum and end the epidemic of HIV within the FBOP including a review of methodology to implement an HIV preexposure prophylaxis program.

Case-area targeted interventions during a large-scale cholera epidemic: A prospective cohort study in Northeast Nigeria.

BACKGROUND: Cholera outbreaks are on the rise globally, with conflict-affected settings particularly at risk. Case-area targeted interventions (CATIs), a strategy whereby teams provide a package of interventions to case and neighboring households within a predefined "ring," are increasingly employed in cholera responses. However, evidence on their ability to attenuate incidence is limited. METHODS AND FINDINGS: We conducted a prospective observational cohort study in 3 conflict-affected states in Nigeria in 2021. Enumerators within rapid response teams observed CATI implementation during a cholera outbreak and collected data on household demographics; existing water, sanitation, and hygiene (WASH) infrastructure; and CATI interventions. Descriptive statistics showed that CATIs were delivered to 46,864 case and neighbor households, with 80.0% of cases and 33.5% of neighbors receiving all intended supplies and activities, in a context with operational challenges of population density, supply stock outs, and security constraints. We then applied prospective Poisson space-time scan statistics (STSS) across 3 models for each state: (1) an unadjusted model with case and population data; (2) an environmentally adjusted model adjusting for distance to cholera treatment centers and existing WASH infrastructure (improved water source, improved latrine, and handwashing station); and (3) a fully adjusted model adjusting for environmental and CATI variables (supply of Aquatabs and soap, hygiene promotion, bedding and latrine disinfection activities, ring coverage, and response timeliness). We ran the STSS each day of our study period to evaluate the space-time dynamics of the cholera outbreaks. Compared to the unadjusted model, significant cholera clustering was attenuated in the environmentally adjusted model (from 572 to 18 clusters) but there was still risk of cholera transmission. Two states still yielded significant clusters (range 8-10 total clusters, relative risk of 2.2-5.5, 16.6-19.9 day duration, including 11.1-56.8 cholera cases). Cholera clustering was completely attenuated in the fully adjusted model, with no significant anomalous clusters across time and space. Associated measures including quantity, relative risk, significance, likelihood of recurrence, size, and duration of clusters reinforced the results. Key limitations include selection bias, remote data monitoring, and the lack of a control group. CONCLUSIONS: CATIs were associated with significant reductions in cholera clustering in Northeast Nigeria despite operational challenges. Our results provide a strong justification for rapid implementation and scale-up CATIs in cholera-response, particularly in conflict settings where WASH access is often limited.

Enteroviruses: epidemic potential, challenges and opportunities with vaccines.

Enteroviruses (EVs) are the most prevalent viruses in humans. EVs can cause a range of acute symptoms, from mild common colds to severe systemic infections such as meningitis, myocarditis, and flaccid paralysis. They can also lead to chronic diseases such as cardiomyopathy. Although more than 280 human EV serotypes exist, only four serotypes have licenced vaccines. No antiviral drugs are available to treat EV infections, and global surveillance of EVs has not been effectively coordinated. Therefore, poliovirus still circulates, and there have been alarming epidemics of non-polio enteroviruses. Thus, there is a pressing need for coordinated preparedness efforts against EVs.This review provides a perspective on recent enterovirus outbreaks and global poliovirus eradication efforts with continuous vaccine development initiatives. It also provides insights into the challenges and opportunities in EV vaccine development. Given that traditional whole-virus vaccine technologies are not suitable for many clinically relevant EVs and considering the ongoing risk of enterovirus outbreaks and the potential for new emerging pathogenic strains, the need for new effective and adaptable enterovirus vaccines is emphasized.This review also explores the difficulties in translating promising vaccine candidates for clinical use and summarizes information from published literature and clinical trial databases focusing on existing enterovirus vaccines, ongoing clinical trials, the obstacles faced in vaccine development as well as the emergence of new vaccine technologies. Overall, this review contributes to the understanding of enterovirus vaccines, their role in public health, and their significance as a tool for future preparedness.

Unlocking influenza B: exploring molecular biology and reverse genetics for epidemic control and vaccine innovation.

Influenza is a highly contagious acute viral illness that affects the respiratory system, posing a significant global public health concern. Influenza B virus (IBV) causes annual seasonal epidemics. The exploration of molecular biology and reverse genetics of IBV is pivotal for understanding its replication, pathogenesis, and evolution. Reverse genetics empowers us to purposefully alter the viral genome, engineer precise genetic modifications, and unveil the secrets of virulence and resistance mechanisms. It helps us in quickly analyzing new virus strains by viral genome manipulation and the development of innovative influenza vaccines. Reverse genetics has been employed to create mutant or reassortant influenza viruses for evaluating their virulence, pathogenicity, host range, and transmissibility. Without this technique, these tasks would be difficult or impossible, making it crucial for preparing for epidemics and protecting public health. Here, we bring together the latest information on how we can manipulate the genes of the influenza B virus using reverse genetics methods, most importantly helper virus-independent techniques.

Ebola virus disease in children: epidemiology, pathogenesis, management, and prevention.

Ebola disease is a severe disease with extremely high case-fatality rates ranging from 28-100%. Observations made during the 2013-2016 West African epidemic improved our understanding of the clinical course of Ebola disease and accelerated the study of therapeutic and preventative strategies. The epidemic also highlighted the unique challenges associated with providing optimal care for children during Ebola disease outbreaks. In this review, we outline current understanding of Ebola disease epidemiology, pathogenesis, management, and prevention, highlighting data pertinent to the care of children. IMPACT: In this review, we summarize recent advancements in our understanding of Ebola disease epidemiology, clinical presentation, and therapeutic and preventative strategies. We highlight recent data pertinent to the care of children and pregnant women and identify research gaps for this important emerging viral infection in children.

Rational social distancing policy during epidemics with limited healthcare capacity.

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be 'overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Modeling the HIV epidemic in MSM in Cyprus: reaching only the 95-95-95 cascade of care targets fails to reduce HIV incidence by 90% in 2030.

National responses should be improved and accelerated to meet the target of ending the Acquired ImmunoDeficiency Syndrome (AIDS) epidemic by 2030. In the Republic of Cyprus, Men who have Sex with Men (MSM) are disproportionately affected by Human Immunodeficiency Virus (HIV), accounting approximately for half of all annual HIV diagnoses. This study assesses the evolution of HIV incidence in MSM in Cyprus until 2030 using a model calibrated to Cypriot epidemiological data. Four scenarios were examined: status quo, two scenarios focusing on introducing Pre-Exposure Prophylaxis (PrEP), and a 90% HIV incidence reduction scenario. Reaching only the 95-95-95 HIV cascade of care targets among MSM would reduce HIV incidence by 48.6% by 2030 compared to 2015. Initiating a PrEP intervention only for high risk MSM would cause a modest further reduction in HIV incidence. To meet the 90% reduction target, PrEP should be expanded to both high and medium risk MSM and, after 2025, behavioral interventions should be implemented so as high-risk MSM gradually move to the medium-risk category. Cyprus will not reach the HIV incidence reduction target by 2030 unless PrEP is gradually promoted and delivered to all high and medium risk MSM along with awareness and behavioral interventions.

Strategic investment and program effectiveness: a roadmap to ending AIDS in Bangladesh by 2030.

The journey towards ending AIDS epidemic in Bangladesh by 2030 is ambitious yet achievable. Although Bangladesh has always had a low rate of HIV among its general population, it remains one of seven countries in Asia and the Pacific where new HIV cases are rising. This study evaluates the effectiveness of HIV programmatic strategies and investment scenarios using the AIDS Epidemic Model (AEM) from 2023 to 2030, focusing on optimizing resource allocation and interventions. The findings indicate that without improved program effectiveness, new HIV infections will increase to 1,382 by 2030, failing to meet the targets of the Global AIDS Strategy 2021-2026. If Bangladesh improves its HIV program effectiveness according to the Global AIDS Strategy 2021-2026, the NSP and Global AIDS Strategy targets could significantly lower new infections and AIDS-related deaths and increase treatment coverage to meet Ending AIDS targets. The NSP targets could reduce new HIV infections to under 275 annually and achieve treatment goals by 2030. The study reveals that NSP targets are the most cost-effective, offering the highest benefit-cost ratio, highlighting the urgent need to enhance HIV prevention program effectiveness, particularly among key populations, to achieve both public health and economic benefits.