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Wang MH, Onnela JP. Flexible Bayesian inference on partially observed epidemics. JOURNAL OF COMPLEX NETWORKS 2024; 12:cnae017. [PMID: 38533184 PMCID: PMC10962317 DOI: 10.1093/comnet/cnae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 03/02/2024] [Indexed: 03/28/2024]
Abstract
Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this article, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC, which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioural change after positive tests or false test results.
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Affiliation(s)
- Maxwell H Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
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2
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Goyal R, De Gruttola V, Onnela JP. Framework for converting mechanistic network models to probabilistic models. JOURNAL OF COMPLEX NETWORKS 2023; 11:cnad034. [PMID: 37873517 PMCID: PMC10588735 DOI: 10.1093/comnet/cnad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/25/2023] [Indexed: 10/25/2023]
Abstract
There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.
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Affiliation(s)
- Ravi Goyal
- Division of Infectious Diseases and Global Public, Health, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Victor De Gruttola
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA USA
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3
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Jones RM, Snead R, Sarwer DB, Ibrahim JK. Mask Adherence and the Relationship Between Masking and Weather-Related Metrics. J Community Health 2023; 48:761-768. [PMID: 37097507 PMCID: PMC10126535 DOI: 10.1007/s10900-023-01219-3] [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] [Accepted: 04/01/2023] [Indexed: 04/26/2023]
Abstract
Little is known about adherence to COVID-19 masking mandates on college campuses or the relationship between weather-related variables and masking. This study aimed to (1) observe students' adherence to on-campus mask mandates and (2) estimate the effect of weather on mask-wearing. Temple University partnered in the Centers for Disease Control and Prevention's observational Mask Adherence Surveillance at Colleges and Universities Project. February-April 2021, weekly observations were completed at 12 on-campus locations to capture whether individuals wore masks, wore them correctly, and the type of mask worn. Fashion and university masks also were recorded. Weekly average temperature, humidity, and precipitation were calculated. Descriptive statistics were calculated for masking adherence overall, over time, and by location. Statistical significance was assessed between correct mask use and mask type and the linear relationships between weekly weather metrics and mask use. Overall, 3508 individuals were observed with 89.6% wearing masks. Of those, 89.4% correctly wore masks. Cloth (58.7%) and surgical masks (35.3%) were most commonly observed and 21.3% wore fashion masks. N95/KN95 masks were correctly worn in 98.3% of observations and surgical and cloth masks were correctly worn ~ 90% of the time. Weekly adherence varied over time and by campus location. Significant inverse linear relationships existed between weekly temperature (r = - 0.72; p < 0.05) and humidity (r = - 0.63; p ≤ 0.05) and masking. Mask adherence and correct use was high. Temperature and humidity inversely affected adherence. Adherence varied by on-campus location, which suggests the locations (e.g., academic buildings, recreational center) and possibly the characteristics of individuals who frequent certain areas impacted adherence.
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Affiliation(s)
- Resa M Jones
- Department of Epidemiology and Biostatistics, College of Public Health, Temple University, 1301 Cecil B. Moore Ave. Ritter Annex, 9thFloor, Philadelphia, PA, 19122, USA.
- Fox Chase Cancer Center, Temple University Health, Philadelphia, PA, USA.
| | - Ryan Snead
- Department of Epidemiology and Biostatistics, College of Public Health, Temple University, 1301 Cecil B. Moore Ave. Ritter Annex, 9thFloor, Philadelphia, PA, 19122, USA
| | - David B Sarwer
- Department of Social and Behavioral Sciences, College of Public Health, Temple University, Philadelphia, PA, USA
| | - Jennifer K Ibrahim
- Department of Health Services, Administration, and Policy, College of Public Health, Temple University, Philadelphia, PA, USA
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Marmor Y, Abbey A, Shahar Y, Mokryn O. Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model. Sci Rep 2023; 13:12955. [PMID: 37563358 PMCID: PMC10415258 DOI: 10.1038/s41598-023-39817-9] [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: 08/12/2022] [Accepted: 07/31/2023] [Indexed: 08/12/2023] Open
Abstract
Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.
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Affiliation(s)
- Yanir Marmor
- Information Systems, University of Haifa, Haifa, Israel
| | - Alex Abbey
- Information Systems, University of Haifa, Haifa, Israel
| | - Yuval Shahar
- Software and Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel
| | - Osnat Mokryn
- Information Systems, University of Haifa, Haifa, Israel.
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Soda KJ, Chen X, Feinn R, Hill DR. Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19. PLoS One 2023; 18:e0280979. [PMID: 37196023 PMCID: PMC10191342 DOI: 10.1371/journal.pone.0280979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.
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Affiliation(s)
- K. James Soda
- Department of Mathematics and Statistics, Quinnipiac University, Hamden, Connecticut, United States of America
| | - Xi Chen
- Department of Sociology and Anthropology, Quinnipiac University, Hamden, Connecticut, United States of America
| | - Richard Feinn
- Department of Medical Sciences, Frank H. Netter MD School of Medicine, Quinnipiac University, Hamden, Connecticut, United States of America
| | - David R. Hill
- Department of Medical Sciences, Frank H. Netter MD School of Medicine, Quinnipiac University, Hamden, Connecticut, United States of America
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Das Swain V, Xie J, Madan M, Sargolzaei S, Cai J, De Choudhury M, Abowd GD, Steimle LN, Prakash BA. Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses. Front Digit Health 2023; 5:1060828. [PMID: 37260525 PMCID: PMC10227502 DOI: 10.3389/fdgth.2023.1060828] [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: 10/03/2022] [Accepted: 04/12/2023] [Indexed: 06/02/2023] Open
Abstract
Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.
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Affiliation(s)
- Vedant Das Swain
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Maanit Madan
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Sonia Sargolzaei
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - James Cai
- Department of Computer Science, Brown University, Providence, RI, United States
| | - Munmun De Choudhury
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Gregory D. Abowd
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
- College of Engineering, Northeastern University, Boston, MA, United States
| | - Lauren N. Steimle
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
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Pei Y, Guo Y, Wu T, Liang H. Quantifying the dynamic transmission of COVID-19 asymptomatic and symptomatic infections: Evidence from four Chinese regions. Front Public Health 2022; 10:925492. [PMID: 36249263 PMCID: PMC9557086 DOI: 10.3389/fpubh.2022.925492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/07/2022] [Indexed: 01/24/2023] Open
Abstract
The dynamic transmission of asymptomatic and symptomatic COVID-19 infections is difficult to quantify because asymptomatic infections are not readily recognized or self-identified. To address this issue, we collected data on asymptomatic and symptomatic infections from four Chinese regions (Beijing, Dalian, Xinjiang, and Guangzhou). These data were considered reliable because the government had implemented large-scale multiple testing during the outbreak in the four regions. We modified the classical susceptible-exposure-infection-recovery model and combined it with mathematical tools to quantitatively analyze the number of infections caused by asymptomatic and symptomatic infections during dynamic transmission, respectively. The results indicated that the ratios of the total number of asymptomatic to symptomatic infections were 0.13:1, 0.48:1, 0.29:1, and 0.15:1, respectively, in the four regions. However, the ratio of the total number of infections caused by asymptomatic and symptomatic infections were 4.64:1, 6.21:1, 1.49:1, and 1.76:1, respectively. Furthermore, the present study describes the daily number of healthy people infected by symptomatic and asymptomatic transmission and the dynamic transmission process. Although there were fewer asymptomatic infections in the four aforementioned regions, their infectivity was found to be significantly higher, implying a greater need for timely screening and control of infections, particularly asymptomatic ones, to contain the spread of COVID-19.
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Affiliation(s)
- Yuanyuan Pei
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China,*Correspondence: Yuanyuan Pei
| | - Yi Guo
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Tong Wu
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Huiying Liang
- Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China,Medical Research Department, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China,Huiying Liang
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Cator D, Huang Q, Mondal A, Ndeffo-Mbah M, Gurarie D. Individual-based modeling of COVID-19 transmission in college communities. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:13861-13877. [PMID: 36654071 DOI: 10.3934/mbe.2022646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.
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Affiliation(s)
- Durward Cator
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX 77840, USA
| | - Qimin Huang
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Anirban Mondal
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Martial Ndeffo-Mbah
- Department of Veterinary and Integrative Biosciences, College of Veterinary and Biomedical Sciences, Texas A & M University, College Station, TX 77840, USA
| | - David Gurarie
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA
- Center for Global Health and Diseases, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
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Hambridge HL, Kahn R, Onnela JP. Effect of a two-dose vs three-dose vaccine strategy in residential colleges using an empirical proximity network. Int J Infect Dis 2022; 119:210-213. [PMID: 35405350 PMCID: PMC8989661 DOI: 10.1016/j.ijid.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 02/04/2023] Open
Affiliation(s)
- Hali L Hambridge
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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