About

Calistus Ngonghala is an Associate Professor of Mathematical Biology in the Department of Mathematics at the University of Florida. His areas of interest and research include the ecology of poverty and disease, infectious disease modeling, and integrating disease epidemiology with human behavior and socio-economic factors. His work involves the study of nonlinear dynamical systems and chaos, contributing to a deeper understanding of the complex interactions between infectious diseases and economic growth. He is actively involved in organizing and participating in conferences and workshops, such as the Hands-on Undergraduate Workshop exploring the interplay between infectious diseases and economic growth, held at the University of Florida. His contact information includes an email address (calistusnn@ufl.edu) and a phone number ((352) 294-2335). Ngonghala's research aims to address critical issues at the intersection of epidemiology, socio-economic factors, and mathematical modeling, contributing valuable insights into the dynamics of poverty, disease, and economic development.

Research topics

• Medicine
• Sociology
• Computer Science
• Management science
• Biology
• Econometrics
• Immunology
• Engineering
• Environmental health
• Mathematics
• Geography
• Demography

Selected publications

• Unveiling the hidden threat: the impact of sub-optimum treatment on acquired immunity, asymptomatic cases, and malaria dynamics

  medRxiv · 2026-03-26

  articleOpen accessSenior authorCorresponding

  Abstract Malaria persists worldwide, exerting its greatest impact in sub-Saharan Africa. This study develops and uses a mathematical model to assess how sub-optimum versus optimum treatment of malaria drives asymptomatic infections, immunity build-up, and sustained transmission, providing insights for effective control. Fitted to case data from Kenya and Nigeria, the framework is used to quantify the burden of malaria and the additional cost associated with sub-optimum treatment. Global sensitivity analysis identifies mosquito demographic parameters, biting rates, and malaria treatment rate among major disease drivers under sub-optimum treatment, emphasizing the need for integrated strategies that improve access to optimum treatment and reduce mosquito–human contact. Model simulations show that sub-optimum treatment amplifies asymptomatic prevalence, sustaining/increasing malaria transmission and burden. Further simulations reveal that optimum treatment could avert more than one-third of infections and deaths, while asymptomatic infections contribute up to 96% (75%) of malaria-related Years Lived with Disability in Kenya (Nigeria). Cost analysis shows that optimum treatment lowers malaria burden significantly and can reduce annual total treatment costs by ≈ $12 million, underscoring the substantial economic and public health gains of limiting sub-optimum care. This study demonstrates that effective and sustained malaria control requires strengthening adherence to treatment, minimizing sub-optimum treatment, reducing mosquito–human contact, and targeting asymptomatic carriers to curb hidden transmission and reduce long-term health and economic losses.

  PublisherOA PDFDOI

• Mathematical Assessment of Wastewater-Based Epidemiology to Predict SARS-CoV-2 Cases and Hospitalizations in Miami-Dade County

  Acta Biotheoretica · 2025-02-11 · 5 citations

  article
  PublisherDOI

• Unmasking Asymptomatic Malaria: The Role of Acquired Immunity and Integrated Interventions in Sustained Transmission Control

  Research Square · 2025-06-06

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Revealing hidden drivers of Lassa fever through a model-informed approach for reproducing and predicting disease dynamics and guiding control strategies

  Scientific Reports · 2025-09-30 · 2 citations

  articleOpen accessSenior author

  Lassa fever (LF), caused by the Lassa virus and transmitted primarily by Mastomys natalensis rodents, is a severe hemorrhagic disease endemic to West Africa, particularly Nigeria, with significant morbidity and mortality rates. This study develops dynamic models for LF, incorporating crucial but often overlooked factors such as vertical transmission (i.e., transmission from parents to their offsprings) in rodents, surface contamination, and asymptomatic human carriers. The persistence of the disease is shown analytically. Using data from Nigeria to train the models, the impact of various control and mitigation measures is assessed. The results of the study reveal that asymptomatic individuals are key drivers of LF and that including additional LF virus transmission pathways, e.g., vertical transmission and environmental contamination, increases the estimated reproduction number threefold compared to previous studies. Models incorporating rodent dynamics show the highest disease prevalence, highlighting the critical role of rodent control. Specifically, effective interventions using only rodent control measures require maintaining rodent populations below a specific threshold. In addition, a multifaceted approach, combining antiviral treatment, environmental disinfection, and personal protective equipment, significantly enhances disease control, while the introduction of a competitor rodent species can drastically reduce human and rodent infections. Ultimately, the study underscores the need for integrated, multifaceted strategies, including targeting rodents, asymptomatic cases, and comprehensive treatment and disinfection protocols, for effective LF management.

  PublisherOA PDFDOI

• Unmasking asymptomatic malaria: The role of acquired immunity and integrated interventions in sustained transmission control

  Applied Mathematical Modelling · 2025-12-04

  articleSenior authorCorresponding
  PublisherDOI

• Impact of vaccination behavior on COVID-19 dynamics and economic outcomes

  Mathematical Biosciences & Engineering · 2025-01-01 · 1 citations

  articleOpen access

  COVID-19 is a highly transmissible respiratory disease that has significantly impacted global health and economies. In this study, we investigated the impact of immunity duration, vaccination behavior, transmission reduction measures, and healthcare timing and duration on COVID-19 dynamics and economic outcomes. Using a mathematical model that integrates epidemiological, human behavioral, and economic factors, we analyzed the effectiveness of interventions based on real-world data. Analytical results revealed up to six disease-free equilibria, with stability determined by reproduction number thresholds. Results from numerical simulations of the model indicated that prolonged immunity and high vaccination rates can reduce peak infections and deaths, whereas delayed hospitalizations and increased transmission can exacerbate outbreaks. Sensitivity analysis highlights vaccine efficacy and uptake as key determinants of disease control. These findings underscore the need for sustained vaccination, timely healthcare interventions, and strategic public health measures.

  PublisherDOI

• Modeling the synergistic interplay between malaria dynamics and economic growth

  Mathematical Biosciences · 2024-04-03 · 3 citations

  article1st authorCorresponding
  PublisherDOI

• Resolve Lassa Fever Persistence: A Compartmental Model with Environmental Virus-Host-Vector Interaction.

  Research Square · 2024-10-15

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Mathematics in medical and life sciences: special issue on behavioural epidemiology

  Mathematics in Medical and Life Sciences · 2024-03-11

  articleOpen access
  PublisherOA PDFDOI

• Mathematical assessment of wastewater-based epidemiology to predict SARS-CoV-2 cases and hospitalizations in Miami-Dade County

  medRxiv · 2024-04-16 · 1 citations

  preprintOpen access

  Abstract This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county’s wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation (with a correlation coefficient r = 0.99) between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model’s prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model’s projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function - the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: A New Multiscale Framework for Integrating Socio-Economic Processes, Vector-Borne Disease Control, and the Impact of Transient Events

  NSF · $160k · 2022–2026

Frequent coauthors

• Matthew H. Bonds

  Harvard University

  95 shared

• Andrés Garchitorena

  55 shared

• Abba B. Gumel

  University of Pretoria

  43 shared

• Benjamín Roche

  Maladies Infectieuses et Vecteurs: Écologie, Génétique, Évolution et Contrôle

  34 shared

• Jean‐François Guégan

  Institut de Recherche pour le Développement

  31 shared

• Gaëtan Texier

  Aix-Marseille Université

  18 shared

• Orou G. Gaoue

  16 shared

• Elinambinina Rajaonarifara

  Sorbonne Université

  14 shared