Amna Tariq

All Publications

• Risk factors associated with dengue and chikungunya seroprevalence and seroconversion among urban populations in western and coastal Kenya. PLoS neglected tropical diseases Bayrau, B. A., Ichura, C., Tariq, A., Winter, C. A., Amugongo, J. S., Okuta, V. A., Mwambingu, L. W., Ogamba, K. O., Shaita, K. N., Ronga, C. O., Chebii, P., Malumbo, S. L., Godana, O. K., Jembe, Z. J., Ng'ang'a, C. M., Mshahame, M. M., Buyukcangaz, E., Migliore, E., Bisanzio, D., Ndenga, B., Mutuku, F., LaBeaud, A. D. 2025; 19 (11): e0013740

  Abstract

  BACKGROUND: Dengue virus (DENV) and chikungunya virus (CHIKV) are arboviruses that are endemic to Kenya. Most DENV infections are asymptomatic resulting in underreporting of cases and symptomatic cases are often misdiagnosed as malaria. Past studies focusing on arboviruses in Kenya are mostly limited to outbreak periods, leaving a gap in knowledge about inter-epidemic arboviral prevalence and associated risk factors. In this study, we aim to determine the risk factors for seroprevalence of and seroconversion to DENV and CHIKV among urban populations in two sites in Kenya.METHODOLOGY/PRINCIPAL FINDINGS: In this prospective cohort study, 4,529 participants were recruited by household from two urban sites in Kenya: Kisumu in the west and Ukunda in the coast. Participants were followed from December 2019 until February 2022 at 6-month intervals. Questionnaire data and blood samples were collected for demographic and serologic data, respectively. If a participant had a febrile illness during the study, they were registered for a sick visit, treated and blood samples were taken to test for acute DENV or CHIKV infection by RT-PCR. Our results showed a 22.8% (1,033/4529) seropositivity rate for DENV and a 21.4% (969/4,529) seropositivity rate for CHIKV; 9% (409/4529) were found to be seropositive for both. DENV and CHIKV seropositivity was more common on the coast (43.9% vs. 6% with p<0.01for DENV, 22.6% vs. 20.5% with p=0.09 for CHIKV) than in the west and among adults than children (30.8% vs 11.5% with p<0.01 for DENV, 32.4% vs 5.9% with p<0.01 for CHIKV). Of the total participants, 4% (183/4529) and 3% (136/4529) seroconverted for DENV and CHIKV, respectively, during the 2-year study period. In our multivariate analysis, controlling for variables in a stepwise selection, being from the coastal site and of older age were the main risk factors for DENV seropositivity while being from the coastal site, having greater levels of education, and crowding in the household were significant risk factors for CHIKV seropositivity. In those participants who were newly exposed to these viruses during the study period, being from the coastal site, high socioeconomic status (SES), and not having window screens in the household were the significant risk factors for both DENV and CHIKV seroconversion.CONCLUSIONS/SIGNIFICANCE: Our results show significant DENV and CHIKV seropositivity among adults and children in urban western and coastal Kenya and evidence of active circulation of both DENV and CHIKV between 2019 and 2022. There were higher rates of seropositivity and active circulation on the coast where past outbreaks have occurred. Although lower education and socioeconomic status (SES) were reported as risk factors for arboviral infections in the past, we found more risk of seropositivity among individuals with higher SES and education, demonstrating the community-wide risk of seropositivity in urban settings. Our findings highlight the need for active surveillance of arboviruses and interventions in Kenya, especially on the coast.

  View details for DOI 10.1371/journal.pntd.0013740

  View details for PubMedID 41284658

• Modelling the effects of precipitation and temperature on malaria incidence in coastal and western Kenya. Malaria journal Tariq, A., Bisanzio, D., Mutuku, F., Ndenga, B., Jembe, Z., Maina, P., Chebii, P., Ronga, C., Okuta, V., LaBeaud, A. D. 2025; 24 (1): 208

  Abstract

  Malaria continues to plague sub-Saharan Africa despite great efforts geared towards its mitigation. In Kenya alone, 70% of the population remains at risk for malaria every year. Malaria is spread by Anopheles mosquitoes carrying the Plasmodium parasite, and displays a complex ecology with various socio-economic, biophysical factors and meteorological predictors, particularly temperature and precipitation, associated with the occurrence of the disease.This study estimated the empirical relationship of temperature and precipitation on the temporal population dynamics of symptomatic malaria cases in Kenyan children living in Ukunda (on Kenyan southern coast), and Kisumu (on Kenyan lake zone) between 2014 and 2022 using daily malaria incidence data collected during a febrile illness surveillance study, merged with daily climatological data collected from ground devices. Generalized additive mixed models (GAMMs) were used to explore the relationship between malaria cases and temperature and precipitation, with Poisson, zero-inflated Poisson and negative binomial distribution and a logarithmic link function. The cross-correlation function assessed the time lags with peak correlations between malaria incidence, precipitation and temperature.The data showed 673 positive malaria incident cases amongst children in Kisumu compared to 1209 cases in Ukunda. The results indicate a positive correlation of malaria incidence with rainfall and temperature in Kisumu and a positive correlation between malaria incidence and rainfall and a negative correlation between malaria incidence and temperature in Ukunda. The lags between malaria incidence and rainfall were similar for Kisumu and Ukunda and estimated between 7 and 15 weeks. With a time lag of 15 weeks in Ukunda, GAMM depicted a steady relationship between rainfall and malaria cases until rainfall reaches 150 mm and the relationship between malaria cases and temperature peaks at 26-27 °C. In Kisumu using a time lag of 15 weeks in the GAMM, a steady relationship between rainfall and malaria cases was observed until almost 120 mm of rainfall, peaking at 160 mm of rainfall and the relationship between malaria cases and temperature remained steady between 22 and 30 °C.Assessing the changes in malaria case incidence due to changing seasonality and weather patterns provides policymakers with updated information to strategize malaria control policies.

  View details for DOI 10.1186/s12936-025-05428-0

  View details for PubMedID 40598170

  View details for PubMedCentralID 7204584

• Understanding the factors contributing to dengue virus and chikungunya virus seropositivity and seroconversion among children in Kenya. PLoS neglected tropical diseases Tariq, A., Khan, A., Mutuku, F., Ndenga, B., Bisanzio, D., Grossi-Soyster, E. N., Jembe, Z., Maina, P., Chebii, P., Ronga, C., Okuta, V., LaBeaud, A. D. 2024; 18 (11): e0012616

  Abstract

  Dengue virus (DENV) and chikungunya virus (CHIKV) are causes of endemic febrile disease among Kenyan children. The exposure risk to these infections is highly multifactorial and linked to environmental factors and human behavior. We investigated relationships between household, socio-economic, demographic, and behavioral risk factors for DENV and CHIKV seropositivity and seroconversion in four settlements in Kenya. We prospectively followed a pediatric cohort of 3,445 children between 2014-2018. We utilized the Kaplan-Meier curves to describe the temporal patterns of seroconversion among tested participants. We employed logistic regression built using generalized linear mixed models, to identify potential exposure risk factors for DENV and CHIKV seroconversion and seropositivity. Overall, 5.2% children were seropositive for DENV, of which 59% seroconverted during the study period. The seroprevalence for CHIKV was 9.2%, of which 54% seroconverted. The fraction of seroconversions per year in the study cohort was <2% for both viruses. Multivariable analysis indicated that older age and the presence of water containers ((OR: 1.15 [95% CI: 1.10, 1.21]), (OR: 1.50 [95% CI: 1.07, 2.10])) increased the odds of DENV seropositivity, whereas higher wealth (OR: 0.83 [95% CI: 0.73, 0.96]) decreased the odds of DENV seropositivity. Multivariable analysis for CHIKV seropositivity showed older age and the presence of trash in the housing compound to be associated with increased odds of CHIKV seropositivity ((OR: 1.11[95% CI: 1.07, 1.15]), (OR: 1.34 [95% CI: 1.04, 1.73])), while higher wealth decreased the odds of CHIKV seropositivity (OR: 0.74[95% CI: 0.66, 0.83]). A higher wealth index (OR: 0.82 [95% CI: 0.69, 0.97]) decreased the odds of DENV seroconversion, whereas a higher age (OR: 1.08 [95% CI: 1.02, 1.15]) and the presence of water containers in the household (OR: 1.91[95% CI: 1.24, 2.95]) were significantly associated with increased odds of DENV seroconversion. Higher wealth was associated with decreased odds for CHIKV seroconversion (OR: 0.75 [95% CI: 0.66, 0.89]), whereas presence of water containers in the house (OR: 1.57 [95% CI: 1.11, 2.21]) was a risk factor for CHIKV seroconversion. Our study links ongoing CHIKV and DENV exposure to decreased wealth and clean water access, underscoring the need to combat inequity and poverty and further enhance ongoing surveillance for arboviruses in Kenya to decrease disease transmission. The study emphasizes the co-circulation of DENV and CHIKV and calls for strengthening the targeted control strategies of mosquito borne diseases in Kenya including vector control, environmental management, public education, community engagement and personal protection.

  View details for DOI 10.1371/journal.pntd.0012616

  View details for PubMedID 39565798

• SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework. BMC medical research methodology Chowell, G., Tariq, A., Dahal, S., Bleichrodt, A., Luo, R., Hyman, J. M. 2024; 24 (1): 131

  Abstract

  Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US.This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score.We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

  View details for DOI 10.1186/s12874-024-02241-2

  View details for PubMedID 38849766

  View details for PubMedCentralID PMC11157887

• SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework. Infectious Disease Modelling Chowell, G., Dahal, S., Bleichrodt, A., Tariq, A., Hyman, J. M., Luo, R. 2024; 9 (2): 411-436

  Abstract

  An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

  View details for DOI 10.1016/j.idm.2024.02.001

  View details for PubMedID 38385022

  View details for PubMedCentralID PMC10879680

• GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models. Scientific reports Chowell, G., Bleichrodt, A., Dahal, S., Tariq, A., Roosa, K., Hyman, J. M., Luo, R. 2024; 14 (1): 1630

  Abstract

  Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.

  View details for DOI 10.1038/s41598-024-51852-8

  View details for PubMedID 38238407

  View details for PubMedCentralID PMC10796326

• Factors Associated with Chikungunya Infection among Pregnant Women in Grenada, West Indies. The American journal of tropical medicine and hygiene Kiener, M., Cudjoe, N., Evans, R., Mapp-Alexander, V., Tariq, A., Macpherson, C., Noel, T., Gerardin, P., Waechter, R., LaBeaud, A. D. 2023

  Abstract

  Neonates are vulnerable to vector-borne diseases given the potential for mother-to-child congenital transmission. To determine factors associated with chikungunya virus (CHIKV) infection among pregnant women in Grenada, West Indies, a retrospective cohort study enrolled women who were pregnant during the 2014 CHIKV epidemic. In all, 520/688 women (75.5%) were CHIKV IgG positive. Low incomes, use of pit latrines, lack of home window screens, and subjective reporting of frequent mosquito bites were associated with increased risk of CHIKV infection in bivariate analyses. In the multivariate modified Poisson regression model, low income (adjusted relative risk [aRR]: 1.05 [95% CI: 1.01-1.10]) and frequent mosquito bites (aRR: 1.05 [95% CI: 1.01-1.10]) were linked to increased infection risk. In Grenada, markers of low socioeconomic status are associated with CHIKV infection among pregnant women. Given that Grenada will continue to face vector-borne outbreaks, interventions dedicated to improving living conditions of the most disadvantaged will help reduce the incidence of arboviral infections.

  View details for DOI 10.4269/ajtmh.23-0157

  View details for PubMedID 37253436

• A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks. Research square Chowell, G., Bleichrodt, A., Dahal, S., Tariq, A., Roosa, K., Hyman, J. M., Luo, R. 2023

  Abstract

  Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking.In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score.We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

  View details for DOI 10.21203/rs.3.rs-2724940/v1

  View details for PubMedID 37034746

  View details for PubMedCentralID PMC10081381