Dataset Information

Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping.

ABSTRACT: Explosive epidemics of chikungunya, Zika, and COVID-19 have recently occurred worldwide, all of which featured large proportions of subclinical infections. Spatial studies of infectious disease epidemics typically use symptomatic infections (cases) to estimate incidence rates (cases/total population), often misinterpreting them as infection risks (infections/total population) or disease risks (cases/infected population). We examined these three measures in a pediatric cohort (Nâ‰ˆ3,000) over two chikungunya epidemics and one Zika epidemic and in a household cohort (N=1,793) over one COVID-19 epidemic in Nicaragua. Across different analyses and all epidemics, case incidence rates considerably underestimated both risk-based measures. Spatial infection risk differed from spatial disease risk, and typical case-only approaches precluded a full understanding of the spatial seroprevalence patterns. For epidemics of pathogens that cause many subclinical infections, relying on case-only datasets and misinterpreting incidence rates, as is common, results in substantial bias, a general finding applicable to many pathogens of high human concern.

SUBMITTER: Bustos Carrillo FA

PROVIDER: S-EPMC8328077 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping.

Bustos Carrillo Fausto Andres FA Mercado Brenda Lopez BL Monterrey Jairo Carey JC Collado Damaris D Saborio Saira S Miranda Tatiana T Barilla Carlos C Ojeda Sergio S Sanchez Nery N Plazaola Miguel M Laguna Harold Suazo HS Elizondo Douglas D Arguello Sonia S Gajewski Anna M AM Maier Hannah E HE Latta Krista K Carlson Bradley B Coloma Josefina J Katzelnick Leah L Sturrock Hugh H Balmaseda Angel A Kuan Guillermina G Gordon Aubree A Harris Eva E

medRxiv : the preprint server for health sciences 20220311

Accurate tracing of epidemic spread over space enables effective control measures. We examined three metrics of infection and disease in a pediatric cohort (N≈3,000) over two chikungunya and one Zika epidemic, and in a household cohort (N=1,793) over one COVID-19 epidemic in Managua, Nicaragua. We compared spatial incidence rates (cases/total population), infection risks (infections/total population), and disease risks (cases/infected population). We used generalized additive and mixed-effects m ...[more]

PMID: 34341804

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Project description:The co-circulation of different emerging viral diseases is a big challenge from an epidemiological point of view. The similarity of symptoms, cases of virus co-infection, and cross-reaction can mislead in the diagnosis of the disease. In this article, a new mathematical model for COVID-19, zika, chikungunya, and dengue co-dynamics is developed and studied to assess the impact of COVID-19 on zika, dengue, and chikungunya dynamics and vice-versa. The local and global stability analyses are carried out. The model is shown to undergo a backward bifurcation under a certain condition. Global sensitivity analysis is also performed on the parameters of the model to determine the most dominant parameters. If the zika-related reproduction number ℛ0Z is used as the response function, then important parameters are: the effective contact rate for vector-to-human transmission of zika ( β2h , which is positively correlated), the human natural death rate ( ϑh , positively correlated), and the vector recruitment rate ( Ψv , also positively correlated). In addition, using the class of individuals co-infected with COVID-19 and zika ( ℐCZh ) as response function, the most dominant parameters are: the effective contact rate for COVID-19 transmission ( β1 , positively correlated), the effective contact rate for vector-to-human transmission of zika ( β2h , positively correlated). To control the co-circulation of all the diseases adequately under an endemic setting, time dependent controls in the form of COVID-19, zika, dengue, and chikungunya preventions are incorporated into the model and analyzed using the Pontryagin's principle. The model is fitted to real COVID-19, zika, dengue, and chikungunya datasets for Espirito Santo (a city with the co-circulation of all the diseases), in Brazil and projections made for the cumulative cases of each of the diseases. Through simulations, it is shown that COVID-19 prevention could greatly reduce the burden of co-infections with zika, dengue, and chikungunya. The negative impact of the COVID-19 pandemic on the control of the arbovirus diseases is also highlighted. Furthermore, it is observed that prevention controls for zika, dengue, and chikungunya can significantly reduce the burden of co-infections with COVID-19.

Dataset Information

Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping.

Publications

Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping.

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