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Calistus N. Ngonghala

Researcher at University of Florida

Publications -  67
Citations -  2250

Calistus N. Ngonghala is an academic researcher from University of Florida. The author has contributed to research in topics: Population & Malaria. The author has an hindex of 22, co-authored 58 publications receiving 1546 citations. Previous affiliations of Calistus N. Ngonghala include Harvard University & Emerging Pathogens Institute.

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Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus.

TL;DR: This study shows that early termination of the strict social-distancing measures could trigger a devastating second wave with burden similar to those projected before the onset of the restrictive measures, and emphasizes the important role social- Distancing plays in curtailing the burden of COVID-19.
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Temperature drives Zika virus transmission: evidence from empirical and mathematical models.

TL;DR: It is demonstrated that the predicted thermal minimum for Zika transmission is 5°C warmer than that of dengue, and current global estimates on the environmental suitability for Zika are greatly over-predicting its possible range.
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Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S.?

TL;DR: The prospect of COVID-19 elimination in the US, using the hypothetical vaccine, is greatly enhanced if the vaccination program is combined with other interventions, such as face mask usage and/or social distancing, which significantly reduces the level of the vaccine-induced herd immunity threshold needed to eliminate the pandemic in the United States.
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The impact of bed-net use on malaria prevalence

TL;DR: It is demonstrated that if 75% of the population were to use bed-nets, malaria could be eliminated and more data on the impact of human and mosquito behavior on malaria spread is needed to develop more realistic models and better predictions.
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A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations

TL;DR: A primer for formulating, analysing and simulating mathematical models for understanding the dynamics of COVID-19, the novel coronavirus that emerged from Wuhan city in December 2019 and became the most important public health challenge facing mankind since the 1918 Spanish flu pandemic.