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Mechanistic epidemiologic models are designed to help us systematically examine the implications of various assumptions about a highly nonlinear process that is hard to predict using only intuition. Used appropriately, they can help guide us through this pandemic.

Many mosquito species, including the major malaria vector Anopheles gambiae , naturally undergo multiple reproductive cycles of blood feeding, egg development and egg laying in their lifespan. Such complex mosquito behavior is regularly overlooked when mosquitoes are experimentally infected with malaria parasites, limiting our ability to accurately describe potential effects on transmission. Here, we examine how Plasmodium falciparum development and transmission potential is impacted when infected mosquitoes feed an additional time. We measured P . falciparum oocyst size and performed sporozoite time course analyses to determine the parasite’s extrinsic incubation period (EIP), i.e. the time required by parasites to reach infectious sporozoite stages, in An . gambiae females blood fed either once or twice. An additional blood feed at 3 days post infection drastically accelerates oocyst growth rates, causing earlier sporozoite accumulation in the salivary glands, thereby shortening the EIP (reduction of 2.3 ± 0.4 days). Moreover, parasite growth is further accelerated in transgenic mosquitoes with reduced reproductive capacity, which mimic genetic modifications currently proposed in population suppression gene drives. We incorporate our shortened EIP values into a measure of transmission potential, the basic reproduction number R 0 , and find the average R 0 is higher (range: 10.1%–12.1% increase) across sub-Saharan Africa than when using traditional EIP measurements. These data suggest that malaria elimination may be substantially more challenging and that younger mosquitoes or those with reduced reproductive ability may provide a larger contribution to infection than currently believed. Our findings have profound implications for current and future mosquito control interventions.

Bites of Anopheles mosquitoes transmit Plasmodium falciparum parasites that cause malaria, which kills hundreds of thousands of people every year. Since the turn of this century, efforts to prevent the transmission of these parasites via the mass distribution of insecticide-treated bed nets have been extremely successful, and have led to an unprecedented reduction in deaths from malaria 1 . However, resistance to insecticides has become widespread in Anopheles populations 2 – 4 , which has led to the threat of a global resurgence of malaria and makes the generation of effective tools for controlling this disease an urgent public health priority. Here we show that the development of P. falciparum can be rapidly and completely blocked when female Anopheles gambiae mosquitoes take up low concentrations of specific antimalarials from treated surfaces—conditions that simulate contact with a bed net. Mosquito exposure to atovaquone before, or shortly after, P. falciparum infection causes full parasite arrest in the midgut, and prevents transmission of infection. Similar transmission-blocking effects are achieved using other cytochrome b inhibitors, which demonstrates that parasite mitochondrial function is a suitable target for killing parasites. Incorporating these effects into a model of malaria transmission dynamics predicts that impregnating mosquito nets with Plasmodium inhibitors would substantially mitigate the global health effects of insecticide resistance. This study identifies a powerful strategy for blocking Plasmodium transmission by female Anopheles mosquitoes, which has promising implications for efforts to eradicate malaria. Treatment of female Anopheles gambiae mosquitoes with atovaquone causes arrest of the Plasmodium falciparum parasite in the midgut, and this holds promise for malaria eradication in areas with insecticide-resistant mosquito populations.

Titrating the importance of endogenous and exogenous drivers for host-pathogen systems remains an important research frontier towards predicting future outbreaks. In Japan, respiratory syncytial virus (RSV), a major childhood respiratory pathogen, displayed a sudden, dramatic shift in outbreak seasonality (from winter to fall) in 2016. We use mathematical models to identify processes that could lead to this outcome. In line with previous analyses, we identify a robust quadratic relationship between transmission against mean specific humidity and mean temperature, with maximum transmission occurring at low and high humidity as well as low and high temperature. This drives semiannual patterns of seasonal transmission rates that peak in summer and winter. Under this transmission regime, a subtle increase in population-level susceptibility or transmission can cause a sudden shift in seasonality, where the degree of shift is primarily determined by the interval between the two peaks of seasonal transmission rate. We hypothesize that an increase in children attending childcare facilities may have contributed to the increase in the overall RSV transmission through increased contact rates between susceptible and infected hosts. Our analysis underscores the power of studying infectious disease dynamics to titrate the roles of underlying drivers of dynamical transitions in ecology. The timing of respiratory syncytial virus seasonal epidemic peaks in Japan shifted in 2016-17. Here, the authors use mathematical modelling to evaluate the hypothesis that this change in timing may be due to an increase in use of childcare facilities following a policy change

Nonpharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic have disrupted the dynamics of respiratory syncytial virus (RSV) on a global scale; however, the cycling of RSV subtypes in the pre- and post-pandemic period remains poorly understood. Here, we used a two subtype RSV model supplemented with epidemiological data to study the impact of NPIs on the two circulating subtypes, RSV-A and RSV-B. The model is calibrated to historic RSV subtype data from the United Kingdom and Finland and predicts a tendency for RSV-A dominance over RSV-B immediately following the implementation of NPIs. Using a global genetic dataset, we confirm that RSV-A has prevailed over RSV-B in the post-pandemic period, consistent with a higher R 0 for RSV-A. With new RSV infant monoclonals and maternal and elderly vaccines becoming widely available, these results may have important implications for understanding intervention effectiveness in the context of disrupted subtype dynamics.

ObjectivesTo prospectively characterise: (1) postoperative opioid analgesic prescribing practices; (2) experience of patients undergoing elective ambulatory surgeries and (3) impact of patient risk for medication misuse on postoperative pain management.DesignLongitudinal survey of patients 7 days before and 7–14 days after surgery.SettingAcademic urban safety-net hospital.Participants181 participants recruited, 18 surgeons, follow-up data from 149 participants (82% retention); 54% women; mean age: 49 years.InterventionsNone.Primary and secondary outcome measuresTotal morphine equivalent dose (MED) prescribed and consumed, percentage of unused opioids.ResultsSurgeons postoperatively prescribed a mean of 242 total MED per patient, equivalent to 32 oxycodone (5 mg) pills. Participants used a mean of 116 MEDs (48%), equivalent to 18 oxycodone (5 mg) pills (~145 mg of oxycodone remaining per patient). A 10-year increase in patient age was associated with 12 (95% CI (−2.05 to –0.35)) total MED fewer prescribed opioids. Each one-point increase in the preoperative Graded Chronic Pain Scale was associated with an 18 (6.84 to 29.60) total MED increase in opioid consumption, and 5% (−0.09% to –0.005%) fewer unused opioids. Prior opioid prescription was associated with a 55 (5.38 to –104.82) total MED increase in opioid consumption, and 19% (−0.35% to –0.02%) fewer unused opioids. High-risk drug use was associated with 9% (−0.19% to 0.002%) fewer unused opioids. Pain severity in previous 3 months, high-risk alcohol, use and prior opioid prescription were not associated with postoperative prescribing practices.ConclusionsParticipants with a preoperative history of chronic pain, prior opioid prescription, and high-risk drug use were more likely to consume higher amounts of opioid medications postoperatively. Additionally, surgeons did not incorporate key patient-level factors (eg, substance use, preoperative pain) into opioid prescribing practices. Opportunities to improve postoperative opioid prescribing include system changes among surgical specialties, and patient education and monitoring.

Nirsevimab is a long-acting monoclonal antibody that protects infants and young children against severe respiratory syncytial virus (RSV) disease. Children are eligible for one 50 mg dose, one 100 mg dose, or two 100 mg doses of nirsevimab based on age, weight, time of year, maternal vaccination, and risk of severe disease. In winter 2023/2024, we developed a model to project the number of nirsevimab doses needed to immunize all eligible U.S. children during the 2024/2025 season. We grouped all births from March 2023 through March 2025 into weekly cohorts, partitioned those cohorts based on eligibility criteria, and computed eligibility for each partition. In the absence of maternal RSV vaccination, we estimated U.S. children would be eligible to receive 4.3 million nirsevimab doses in 2024/2025, of which 48% would be 100 mg doses. Projections of total eligibility can be used to inform production goals and avoid shortages of nirsevimab.

Malaria, caused by a single-cell parasite and transmitted by blood-feeding mosquitoes, is one of the world's leading infectious diseases in terms of both morbidity and mortality. While the first two decades of the twenty-first century saw rapid advances in the control and elimination of malaria worldwide, progress has stalled in recent years due to a combination of biological and behavioral factors. A better understanding of these barriers is essential to the future of malaria eradication.Mathematical modeling is an important tool for understanding the dynamics of infectious diseases. Models can lend important insight into challenges for control and elimination, as well as forecast the future of disease transmission. In this dissertation, I apply three mechanistic modeling approaches to understand complexities of malaria transmission, with a focus on incorporating important questions about human behavior and mosquito behavior and biology as they relate to malaria interventions. In Chapter 1, I use a temperature-based model of the basic reproduction number (R0) for malaria to understand the epidemiological impact of a new experimental finding: that the latent period of the malaria parasite in the mosquito is shortened by additional blood meals. I find that the latent period reduction due to multiple blood-feeding leads to an increase in estimated R0, which has important implications for the continued persistence of malaria in low-transmission settings.In Chapter 2, I construct a mosquito population model to simulate insecticide resistance assays from adult-captured mosquito collections, in order to quantify possible biases that may arise in resistance assays. I find that adult-capture assays can be improved using a simple mathematical correction, and are thus a viable alternative to larval assays for resistance monitoring programs.Finally, in Chapter 3, I develop an agent-based model of a small human population with endemic malaria to explore the impact of risk-based decision making on the effectiveness of bed nets against malaria transmission. I consider three scenarios, including one in which people respond to a misperception of disease risk, to demonstrate the importance of considering subjective risk perception in intervention planning models.

Nursing homes and other long-term care facilities have been disproportionately impacted by the COVID-19 pandemic. Strategies are urgently needed to reduce transmission in these high-risk populations. To evaluate COVID-19 transmission in nursing homes associated with contact-targeted interventions and testing. This decision analytical modeling study developed an agent-based susceptible-exposed-infectious (asymptomatic/symptomatic)-recovered model between July and September 2020 to examine SARS-CoV-2 transmission in nursing homes. Residents and staff of a simulated nursing home with 100 residents and 100 staff split among 3 shifts were modeled individually; residents were split into 2 cohorts based on COVID-19 diagnosis. Data were analyzed from September to October 2020. In the resident cohorting intervention, residents who had recovered from COVID-19 were moved back from the COVID-19 (ie, infected with SARS-CoV-2) cohort to the non-COVID-19 (ie, susceptible and uninfected with SARS-CoV-2) cohort. In the immunity-based staffing intervention, staff who had recovered from COVID-19 were assumed to have protective immunity and were assigned to work in the non-COVID-19 cohort, while susceptible staff worked in the COVID-19 cohort and were assumed to have high levels of protection from personal protective equipment. These interventions aimed to reduce the fraction of people's contacts that were presumed susceptible (and therefore potentially infected) and replaced them with recovered (immune) contacts. A secondary aim of was to evaluate cumulative incidence of SARS-CoV-2 infections associated with 2 types of screening tests (ie, rapid antigen testing and polymerase chain reaction [PCR] testing) conducted with varying frequency. Estimated cumulative incidence proportion of SARS-CoV-2 infection after 3 months. Among the simulated cohort of 100 residents and 100 staff members, frequency and type of testing were associated with smaller outbreaks than the cohorting and staffing interventions. The testing strategy associated with the greatest estimated reduction in infections was daily antigen testing, which reduced the mean cumulative incidence proportion by 49% in absence of contact-targeted interventions. Under all screening testing strategies, the resident cohorting intervention and the immunity-based staffing intervention were associated with reducing the final estimated size of the outbreak among residents, with the immunity-based staffing intervention reducing it more (eg, by 19% in the absence of testing) than the resident cohorting intervention (eg, by 8% in the absence of testing). The estimated reduction in transmission associated with these interventions among staff varied by testing strategy and community prevalence. These findings suggest that increasing the frequency of screening testing of all residents and staff, or even staff alone, in nursing homes may reduce outbreaks in this high-risk setting. Immunity-based staffing may further reduce spread at little or no additional cost and becomes particularly important when daily testing is not feasible.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR = 56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.