Modeling Approaches Toward Understanding Infectious Disease Transmission

Skrip, Laura; Townsend, Jeffrey P.

doi:10.1007/978-3-030-25553-4_14

Cited by 5 publications

(2 citation statements)

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“…Mathematical modeling is an epidemiologic tool used to study population dynamics and infectious disease transmission [83,84]. Modeling has increasingly been recognized as an important technique used to inform disease prevention and control efforts.…”

Section: Mathematical Modelingmentioning

confidence: 99%

The Global Emergence of Human Babesiosis

2021

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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.

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Section: Mathematical Modelingmentioning

confidence: 99%

The Global Emergence of Human Babesiosis

2021

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“…Early epidemic models commonly use exponential growth models without containment measures; however, this is less appropriate in the presence of measures ( Tovissodé et al, 2020 ). Sources of further variation include the choice of model, the simulation method, the method of parameter estimation and the assumptions made ( Skrip and Townsend, 2019 ).…”

Section: Introductionmentioning

confidence: 99%