Modeling Approaches Toward Understanding Infectious Disease Transmission

Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.

Why COVID-19 modelling of progression and prevention fails to translate to the real-world

Mathematical models were used widely to inform policy during the COVID pandemic. However, there is a poor understanding of their limitations and how they influence decision-making. We used systematic review search methods to find early modelling studies that determined the reproduction number and analysed its use and application to interventions and policy in the UK. Up to March 2020, we found 42 reproduction number estimates (39 based on Chinese data: R₀ range 2.1-6.47). Several biases affect the quality of modelling studies that are infrequently discussed, and many factors contribute to significant differences in the results of individual studies that go beyond chance. The sources of effect estimates incorporated into mathematical models are unclear. There is often a lack of a relationship between transmission estimates and the timing of imposed restrictions, which is further affected by the lag in reporting. Modelling studies lack basic evidence-based methods that aid their quality assessment, reporting and critical appraisal. If used judiciously, models may be helpful, especially if they openly present the uncertainties and use sensitivity analyses extensively, which need to consider and explicitly discuss the limitations of the evidence. However, until the methodological and ethical issues are resolved, predictive models should be used cautiously.

The Global Emergence of Human Babesiosis

Babesiosis is an emerging tick-borne disease caused by intraerythrocytic protozoa that are primarily transmitted by hard-bodied (ixodid) ticks and rarely through blood transfusion, perinatally, and organ transplantation. More than 100 Babesia species infect a wide spectrum of wild and domestic animals worldwide and six have been identified as human pathogens. Babesia microti is the predominant species that infects humans, is found throughout the world, and causes endemic disease in the United States and China. Babesia venatorum and Babesia crassa-like agent also cause endemic disease in China. Babesia divergens is the predominant species in Europe where fulminant cases have been reported sporadically. The number of B. microti infections has been increasing globally in recent decades. In the United States, more than 2000 cases are reported each year, although the actual number is thought to be much higher. In this review of the epidemiology of human babesiosis, we discuss epidemiologic tools used to monitor disease location and frequency; demographics and modes of transmission; the location of human babesiosis; the causative Babesia species in the Americas, Europe, Asia, Africa, and Australia; the primary clinical characteristics associated with each of these infections; and the increasing global health burden of this disease.