1
|
Espinoza B, Adiga A, Venkatramanan S, Warren AS, Chen J, Lewis BL, Vullikanti A, Swarup S, Moon S, Barrett CL, Athreya S, Sundaresan R, Chandru V, Laxminarayan R, Schaffer B, Poor HV, Levin SA, Marathe MV. Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions. Proc Natl Acad Sci U S A 2023; 120:e2305227120. [PMID: 37983514 PMCID: PMC10691339 DOI: 10.1073/pnas.2305227120] [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: 04/04/2023] [Accepted: 10/13/2023] [Indexed: 11/22/2023] Open
Abstract
Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population.
Collapse
Affiliation(s)
- Baltazar Espinoza
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Aniruddha Adiga
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Srinivasan Venkatramanan
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Andrew Scott Warren
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Jiangzhuo Chen
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Bryan Leroy Lewis
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Anil Vullikanti
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
| | - Samarth Swarup
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Sifat Moon
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Christopher Louis Barrett
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
| | - Siva Athreya
- Indian Statistical Institute, Bengaluru, Karnataka560059, India
- International Centre for Theoretical Sciences, Bengaluru, Karnataka560089, India
| | - Rajesh Sundaresan
- Department of Electrical and Communication Engineering, Indian Institute of Science, Bengaluru, Karnataka560012, India
- Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, Karnataka560012, India
- Centre for Networked Intelligence, Indian Institute of Science, Bengaluru, Karnataka560012, India
| | - Vijay Chandru
- Strand Life Sciences, Bengaluru, Karnataka560024, India
- BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, Karnataka560012, India
| | | | - Benjamin Schaffer
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ08544
| | - H. Vincent Poor
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Madhav V. Marathe
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
| |
Collapse
|
2
|
Alsentzer E, Ballard SB, Neyra J, Vera DM, Osorio VB, Quispe J, Blazes DL, Loayza L. Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting. Emerg Infect Dis 2020; 26:2196-2200. [PMID: 32818406 PMCID: PMC7454051 DOI: 10.3201/eid2609.191315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We evaluated the performance of X-bar chart, exponentially weighted moving average, and C3 cumulative sums aberration detection algorithms for acute diarrheal disease syndromic surveillance at naval sites in Peru during 2007–2011. The 3 algorithms’ detection sensitivity was 100%, specificity was 97%–99%, and positive predictive value was 27%–46%.
Collapse
|
3
|
Alsentzer E, Ballard SB, Neyra J, Vera DM, Osorio VB, Quispe J, Blazes DL, Loayza L. Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting. Emerg Infect Dis 2020. [DOI: 10.3201/eid09.191315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
|
4
|
Velappan N, Daughton AR, Fairchild G, Rosenberger WE, Generous N, Chitanvis ME, Altherr FM, Castro LA, Priedhorsky R, Abeyta EL, Naranjo LA, Hollander AD, Vuyisich G, Lillo AM, Cloyd EK, Vaidya AR, Deshpande A. Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks. JMIR Public Health Surveill 2019; 5:e12032. [PMID: 30801254 PMCID: PMC6409513 DOI: 10.2196/12032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/26/2018] [Accepted: 01/25/2019] [Indexed: 11/20/2022] Open
Abstract
Background Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Lauren A Castro
- Los Alamos National Laboratory, Los Alamos, NM, United States
| | | | | | - Leslie A Naranjo
- Los Alamos National Laboratory, Los Alamos, NM, United States.,Specifica Inc, New Mexico Consortium Biological Laboratory, Los Alamos, NM, United States
| | | | - Grace Vuyisich
- Los Alamos National Laboratory, Los Alamos, NM, United States
| | | | - Emily Kathryn Cloyd
- Los Alamos National Laboratory, Los Alamos, NM, United States.,University of Virginia, Charlottesville, VA, United States
| | | | - Alina Deshpande
- Los Alamos National Laboratory, Los Alamos, NM, United States
| |
Collapse
|
5
|
Daughton AR, Priedhorsky R, Fairchild G, Generous N, Hengartner A, Abeyta E, Velappan N, Lillo A, Stark K, Deshpande A. An extensible framework and database of infectious disease for biosurveillance. BMC Infect Dis 2017; 17:549. [PMID: 28784113 PMCID: PMC5547458 DOI: 10.1186/s12879-017-2650-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 07/28/2017] [Indexed: 02/04/2023] Open
Abstract
Biosurveillance, a relatively young field, has recently increased in importance because of increasing emphasis on global health. Databases and tools describing particular subsets of disease are becoming increasingly common in the field. Here, we present an infectious disease database that includes diseases of biosurveillance relevance and an extensible framework for the easy expansion of the database.
Collapse
|
6
|
Daughton AR, Generous N, Priedhorsky R, Deshpande A. An approach to and web-based tool for infectious disease outbreak intervention analysis. Sci Rep 2017; 7:46076. [PMID: 28417983 PMCID: PMC5394686 DOI: 10.1038/srep46076] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 02/28/2017] [Indexed: 01/03/2023] Open
Abstract
Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.
Collapse
|