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The mosquito-borne disease (malaria) imposes significant challenges on human health, healthcare systems, and economic growth/productivity in many countries. This study develops and analyzes a model to understand the interplay between malaria dynamics, economic growth, and transient events. It uncovers varied effects of malaria and economic parameters on model outcomes, highlighting the interdependence of the reproduction number () on both malaria and economic factors, and a reciprocal relationship where malaria diminishes economic productivity, while higher economic output is associated with reduced malaria prevalence. This emphasizes the intricate interplay between malaria dynamics and socio-economic factors. The study offers insights into malaria control and underscores the significance of optimizing external aid allocation, especially favoring an even distribution strategy, with the most significant reduction observed in an equal monthly distribution strategy compared to longer distribution intervals. Furthermore, the study shows that controlling malaria in high mosquito biting areas with limited aid, low technology, inadequate treatment, or low economic investment is challenging. The model exhibits a backward bifurcation implying that sustainability of control and mitigation measures is essential even when is slightly less than one. Additionally, there is a parameter regime for which long transients are feasible. Long transients are critical for predicting the behavior of dynamic systems and identifying factors influencing transitions; they reveal reservoirs of infection, vital for disease control. Policy recommendations for effective malaria control from the study include prioritizing sustained control measures, optimizing external aid allocation, and reducing mosquito biting. 

Abstract High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that makes it possible to construct confidence sets with thep-value function and to use the same function to check the coverage explicitly at any given parameter point. LikeLF2I, this extension yields provably valid confidence sets in parameter inference problems for which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology³3Code to reproduce all of our results is available onhttps://github.com/AliAlkadhim/ALFFI.. 

Navigating a career as a mathematician in academia, industry, or a national lab was challenging for many families with children before the COVID-19 pandemic. Then, the pandemic hit and the situation was exacerbated. Parents and parents-to-be were tested and challenged in ways unanticipated, with time for parental duties clashing with time for research, teaching, and service, leaving those wishing to be parents contemplating the feasibility of this balancing act of parenthood and work-life in a COVID-19 era and beyond. Many members in our mathematics community experienced these challenges first hand and persevered. Lessons were learned and different methodologies employed as many reimagined what work-life and home-life balance looked like. These lessons and methodologies can be useful in our future endeavors as parent-educators and researchers, and if shared can benefit others who are in parenthood or on the path to parenthood, as they seek to create a better harmony between work and home life. Thus, this article explores and showcases some of the discussions that ensued during a 2022 Joint Mathematics Meeting (JMM) Professional Development Workshop Mathematicians Navigating Parenthood organized by the authors. The article collects key discussion points and lessons learned, putting together useful solutions and resources, as well as unresolved questions. We report on strategies to help parents and parents-to-be succeed as well as present proposals on what departments could implement based on their individual policies to provide a welcoming environment to colleagues with, or expecting, children. 

Increasing temperatures have raised concerns over the potential effect on disease spread. Temperature is a well known factor affecting mosquito population dynamics and the development rate of the malaria parasite within the mosquito, and consequently, malaria transmission. A sinusoidal wave is commonly used to incorporate temperature effects in malaria models, however, we introduce a seasonal malaria framework that links data on temperature-dependent mosquito and parasite demographic traits to average monthly regional temperature data, without forcing a sinusoidal t to the data. We introduce a spline methodology that maps temperature-dependent mosquito traits to time-varying model parameters. The resulting non-autonomous system of differential equations is used to study the impact of seasonality on malaria transmission dynamics and burden in a high and low malaria transmission region in Malawi. We present numerical simulations illustrating how temperature shifts alter the entomological inoculation rate and the number of malaria infections in these regions. 

https://issues.org/new-theory-increasingly-tangled-banks/ Twombly, Saran, Alan Hastings, Tom Miller, Michael Cortez, Karen Abbott, Tanjona Ramiadantsoa, Julie Blackwood, and Olivia Prosper. “New Theory for Increasingly Tangled Banks.” Issues in Science and Technology 38, no. 4 (Summer 2022): 39–44. Theory has fallen out of fashion in the sciences, in favor of data collection and number crunching. But the conceptual frameworks provided by theory are essential for addressing society’s most complex and urgent problems. 

Mosquito-borne diseases, in particular malaria, have a significant burden worldwide leading to nearly half a million deaths each year. The malaria parasite requires a vertebrate host, such as a human, and a vector host, the Anopheles mosquito, to complete its full life cycle. Here, we focus on the parasite dynamics within the vector to examine the first appearance of sporozoites in the salivary glands, which indicates a first time of infectiousness of mosquitoes. The timing of this period of pathogen development in the mosquito until transmissibility, known as the extrinsic incubation period, remains poorly understood. We develop compartmental models of within-mosquito parasite dynamics fitted with experimental data on oocyst and sporozoite counts. We find that only a fraction of oocysts burst to release sporozoites and bursting must be delayed either via a time-dependent function or a gamma-distributed set of compartments. We use Bayesian inference to estimate distributions of parameters and determine that bursting rate is a key epidemiological parameter. A better understanding of the factors impacting the extrinsic incubation period will aid in the development of interventions to slow or stop the spread of malaria.