1
|
Wanyana MW, Akunzirwe R, King P, Atuhaire I, Zavuga R, Lubwama B, Kabami Z, Ahirirwe SR, Ninsiima M, Naiga HN, Zalwango JF, Zalwango MG, Kawungezi PC, Simbwa BN, Kizito SN, Kiggundu T, Agaba B, Migisha R, Kadobera D, Kwesiga B, Bulage L, Ario AR, Harris JR. Performance and impact of contact tracing in the Sudan virus outbreak in Uganda, September 2022-January 2023. Int J Infect Dis 2024; 141:106959. [PMID: 38340782 DOI: 10.1016/j.ijid.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Contact tracing (CT) is critical for ebolavirus outbreak response. Ideally, all new cases after the index case should be previously-known contacts (PKC) before their onset, and spend minimal time ill in the community. We assessed the impact of CT during the 2022 Sudan Virus Disease (SVD) outbreak in Uganda. METHODS We collated anonymized data from the SVD case and contacts database to obtain and analyze data on CT performance indicators, comparing confirmed cases that were PKC and were not PKC (NPKC) before onset. We assessed the effect of being PKC on the number of people infected using Poisson regression. RESULTS There were 3844 contacts of 142 confirmed cases (mean: 22 contacts/case). Forty-seven (33%) confirmed cases were PKC. PKCs had fewer median days from onset to isolation (4 vs 6; P<0.007) and laboratory confirmation (4 vs 7; P<0.001) than NPKC. Being a PKC vs NPKC reduced risk of transmitting infection by 84% (IRR=0.16, 95% CI 0.08-0.32). CONCLUSION Contact identification was sub-optimal during the outbreak. However, CT reduced the time SVD cases spent in the community before isolation and the number of persons infected in Uganda. Approaches to improve contact tracing, especially contact listing, may improve control in future outbreaks.
Collapse
Affiliation(s)
- Mercy Wendy Wanyana
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda.
| | - Rebecca Akunzirwe
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Patrick King
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Immaculate Atuhaire
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Robert Zavuga
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | | | - Zainah Kabami
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Sherry Rita Ahirirwe
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Mackline Ninsiima
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Hellen Nelly Naiga
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Jane Frances Zalwango
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Marie Gorreti Zalwango
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Peter Chris Kawungezi
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Brenda Nakafeero Simbwa
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Saudah Namubiru Kizito
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Thomas Kiggundu
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Brian Agaba
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Richard Migisha
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Daniel Kadobera
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Benon Kwesiga
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | - Lilian Bulage
- Uganda Public Health Fellowship Program-Uganda National Institute of Public Health, Kampala, Uganda
| | | | - Julie R Harris
- Division of Global Health Protection, US Centers for Disease Control and Prevention, Kampala, Uganda
| |
Collapse
|
2
|
Howerton E, Dahlin K, Edholm CJ, Fox L, Reynolds M, Hollingsworth B, Lytle G, Walker M, Blackwood J, Lenhart S. The effect of governance structures on optimal control of two-patch epidemic models. J Math Biol 2023; 87:74. [PMID: 37861753 PMCID: PMC10589198 DOI: 10.1007/s00285-023-02001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Infectious diseases continue to pose a significant threat to the health of humans globally. While the spread of pathogens transcends geographical boundaries, the management of infectious diseases typically occurs within distinct spatial units, determined by geopolitical boundaries. The allocation of management resources within and across regions (the "governance structure") can affect epidemiological outcomes considerably, and policy-makers are often confronted with a choice between applying control measures uniformly or differentially across regions. Here, we investigate the extent to which uniform and non-uniform governance structures affect the costs of an infectious disease outbreak in two-patch systems using an optimal control framework. A uniform policy implements control measures with the same time varying rate functions across both patches, while these measures are allowed to differ between the patches in a non-uniform policy. We compare results from two systems of differential equations representing transmission of cholera and Ebola, respectively, to understand the interplay between transmission mode, governance structure and the optimal control of outbreaks. In our case studies, the governance structure has a meaningful impact on the allocation of resources and burden of cases, although the difference in total costs is minimal. Understanding how governance structure affects both the optimal control functions and epidemiological outcomes is crucial for the effective management of infectious diseases going forward.
Collapse
Affiliation(s)
- Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Kyle Dahlin
- Center for the Ecology of Infectious Diseases, Odum School of Ecology, University of Georgia, Athens, GA, USA.
| | | | - Lindsey Fox
- Mathematics Discipline, Eckerd College, Saint Petersburg, FL, USA
| | - Margaret Reynolds
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | | | - George Lytle
- Department of Biology, Chemistry, Mathematics, and Computer Science, University of Montevallo, Montevallo, AL, USA
| | - Melody Walker
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Julie Blackwood
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| |
Collapse
|
3
|
Agusto FB, Numfor E, Srinivasan K, Iboi EA, Fulk A, Saint Onge JM, Peterson AT. Impact of public sentiments on the transmission of COVID-19 across a geographical gradient. PeerJ 2023; 11:e14736. [PMID: 36819996 PMCID: PMC9938658 DOI: 10.7717/peerj.14736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 12/21/2022] [Indexed: 02/17/2023] Open
Abstract
COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual's belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people's sentiments (positive and negative) which accounts for the influence of disinformation. People's sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.
Collapse
Affiliation(s)
| | - Eric Numfor
- Augusta University, Augusta, Georgia, United States
| | | | | | | | - Jarron M. Saint Onge
- University of Kansas, Lawrence, Kansas, United States,University of Kansas Medical Center, Kansas City, Kansas, United States
| | | |
Collapse
|
4
|
Bouba A, Helle KB, Schneider KA. Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks. PLoS One 2023; 18:e0276351. [PMID: 36649296 PMCID: PMC9844901 DOI: 10.1371/journal.pone.0276351] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/19/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The recent outbreaks of Ebola virus disease (EVD) in Uganda and the Marburg virus disease (MVD) in Ghana reflect a persisting threat of Filoviridae to the global health community. Characteristic of Filoviridae are not just their high case fatality rates, but also that corpses are highly contagious and prone to cause infections in the absence of appropriate precautions. Vaccines against the most virulent Ebolavirus species, the Zaire ebolavirus (ZEBOV) are approved. However, there exists no approved vaccine or treatment against the Sudan ebolavirus (SUDV) which causes the current outbreak of EVD. Hence, the control of the outbreak relies on case isolation, safe funeral practices, and contact tracing. So far, the effectiveness of these control measures was studied only separately by epidemiological models, while the impact of their interaction is unclear. METHODS AND FINDINGS To sustain decision making in public health-emergency management, we introduce a predictive model to study the interaction of case isolation, safe funeral practices, and contact tracing. The model is a complex extension of an SEIR-type model, and serves as an epidemic preparedness tool. The model considers different phases of the EVD infections, the possibility of infections being treated in isolation (if appropriately diagnosed), in hospital (if not properly diagnosed), or at home (if the infected do not present to hospital for whatever reason). It is assumed that the corpses of those who died in isolation are buried with proper safety measures, while those who die outside isolation might be buried unsafely, such that transmission can occur during the funeral. Furthermore, the contacts of individuals in isolation will be traced. Based on parameter estimates from the scientific literature, the model suggests that proper diagnosis and hence isolation of cases has the highest impact in reducing the size of the outbreak. However, the combination of case isolation and safe funeral practices alone are insufficient to fully contain the epidemic under plausible parameters. This changes if these measures are combined with contact tracing. In addition, shortening the time to successfully trace back contacts contribute substantially to contain the outbreak. CONCLUSIONS In the absence of an approved vaccine and treatment, EVD management by proper and fast diagnostics in combination with epidemic awareness are fundamental. Awareness will particularly facilitate contact tracing and safe funeral practices. Moreover, proper and fast diagnostics are a major determinant of case isolation. The model introduced here is not just applicable to EVD, but also to other viral hemorrhagic fevers such as the MVD or the Lassa fever.
Collapse
Affiliation(s)
- Aliou Bouba
- Hochschule Mittweida, University of Applied Sciences Mittweida, Mittweida, Germany
- African Institute for Mathematical Sciences (AIMS), Limbe, Cameroon
| | | | | |
Collapse
|
5
|
Hwang KKL, Edholm CJ, Saucedo O, Allen LJS, Shakiba N. A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics. Bull Math Biol 2022; 84:91. [PMID: 35859080 PMCID: PMC9298711 DOI: 10.1007/s11538-022-01030-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 05/15/2022] [Indexed: 12/31/2022]
Abstract
The dynamic nature of the COVID-19 pandemic has demanded a public health response that is constantly evolving due to the novelty of the virus. Many jurisdictions in the USA, Canada, and across the world have adopted social distancing and recommended the use of face masks. Considering these measures, it is prudent to understand the contributions of subpopulations—such as “silent spreaders”—to disease transmission dynamics in order to inform public health strategies in a jurisdiction-dependent manner. Additionally, we and others have shown that demographic and environmental stochasticity in transmission rates can play an important role in shaping disease dynamics. Here, we create a model for the COVID-19 pandemic by including two classes of individuals: silent spreaders, who either never experience a symptomatic phase or remain undetected throughout their disease course; and symptomatic spreaders, who experience symptoms and are detected. We fit the model to real-time COVID-19 confirmed cases and deaths to derive the transmission rates, death rates, and other relevant parameters for multiple phases of outbreaks in British Columbia (BC), Canada. We determine the extent to which SilS contributed to BC’s early wave of disease transmission as well as the impact of public health interventions on reducing transmission from both SilS and SymS. To do this, we validate our model against an existing COVID-19 parameterized framework and then fit our model to clinical data to estimate key parameter values for different stages of BC’s disease dynamics. We then use these parameters to construct a hybrid stochastic model that leverages the strengths of both a time-nonhomogeneous discrete process and a stochastic differential equation model. By combining these previously established approaches, we explore the impact of demographic and environmental variability on disease dynamics by simulating various scenarios in which a COVID-19 outbreak is initiated. Our results demonstrate that variability in disease transmission rate impacts the probability and severity of COVID-19 outbreaks differently in high- versus low-transmission scenarios.
Collapse
Affiliation(s)
- Karen K. L. Hwang
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC Canada
| | | | - Omar Saucedo
- Department of Mathematics, Virginia Tech, Blacksburg, VA USA
| | - Linda J. S. Allen
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX USA
| | - Nika Shakiba
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC Canada
| |
Collapse
|
6
|
Edholm CJ, Levy B, Spence L, Agusto FB, Chirove F, Chukwu CW, Goldsman D, Kgosimore M, Maposa I, Jane White KA, Lenhart S. A vaccination model for COVID-19 in Gauteng, South Africa. Infect Dis Model 2022; 7:333-345. [PMID: 35702698 PMCID: PMC9181832 DOI: 10.1016/j.idm.2022.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 12/02/2022] Open
Abstract
The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.
Collapse
Affiliation(s)
| | - Benjamin Levy
- Mathematics Department, Fitchburg State University, Fitchburg, MA, USA
| | - Lee Spence
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Folashade B Agusto
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA
| | - Faraimunashe Chirove
- Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa
| | - C Williams Chukwu
- Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa
| | - David Goldsman
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Moatlhodi Kgosimore
- Biometry and Mathematics Department, Botswana University of Agriculture and Natural Resources, Gaborone, Botswana
| | - Innocent Maposa
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - K A Jane White
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| |
Collapse
|