Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar

Kauffman, Kayla; Werner, Courtney S.; Titcomb, Georgia; Pender, Michelle; Rabezara, Jean Yves; Herrera, James P.; Shapiro, Julie Teresa; Solis, Alma; Soarimalala, Voahangy; Tortosa, Pablo; Kramer, Randall A.; Moody, James; Mucha, Peter J.; Nunn, Charles L.

doi:10.1098/rsif.2021.0690

Cited by 11 publications

(13 citation statements)

References 71 publications

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“…This is similar to the approach in Ref. (13) where the authors examine how well networks constructed from different data types effectively capture transmission potential. We show that one cannot generate accurate expectations of epidemic extent from uniplex data, even if combining results across layers to reconstruct the multiplex reality, as an estimate of their potential combined effects.…”

Section: Motivationmentioning

confidence: 90%

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On limitations of uniplex networks for modeling multiplex contagion

Landry¹,

Adams²

2022

Preprint

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Many substantive processes are inherently multiplex in nature, yet are often reduced to processes on uniplex networks in analytic practice. We look at how data modeling choices can affect the predictions of contagion processes. We demonstrate that multiplex contagion processes are not simply the union of contagion processes over their constituent uniplex networks. We use multiplex network data from two different contexts-a behavioral network to represent their potential for infectious disease transmission using a "simple" epidemiological model, the other from online social network site users to represent their potential for information spread using a thresholdbased "complex" contagion process. Our results show that contagion on multiplex data is not represented accurately in models developed from the uniplex networks even when they are combined, and that the nature of the differences between the (combined) uniplex and multiplex results depends on the specific spreading process over these networks. social networks | behavioral networks | contagion processes | multiplexity | infectious diseases | information propagation * Moreover, simply increasing the coverage of the sample used will not overcome these potential sampling biases (7). Significance StatementWhen things like diseases or ideas spread through a population via relationships of different types, attempts to understand the extent of the population they can ultimately reach are undermined by research that looks at only one type of relationship at a time. We use data on sexual/needle-sharing and Twitter/Foursquare relationships to demonstrate the potential limitations that stem from approaches using only one of those at a time or both independently to estimate disease spread or information propagation. N.W.L. generated simulations, analyzed the data, and wrote the article. j.a. designed the research, analyzed the data, and wrote the article.

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Section: Motivationmentioning

confidence: 90%

“…As a more positive recommendation, a primary takeaway of our results is that future data collection efforts should prioritize faithfully capturing the multiplex realities of the underlying processes intended to be examined as in Ref. (13), rather than relying solely on data of a single type.…”

Section: Discussionmentioning

confidence: 99%

On limitations of uniplex networks for modeling multiplex contagion

Landry¹,

Adams²

2022

Preprint

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“…The social contact networks were constructed using survey and GPS tracker data of consenting adults (over 18 years of age) living in two villages in the SAVA region of northeastern Madagascar. The first village, Mandena (14°28'36″ S 47°48'50″ E), as described by Kauffman et al (19), has approximately 2700 people (based on census data from local authorities). The second village, Sarahandrano (14°36'27'' S 49°38'50''), is home to approximately 900 people.…”

Section: Social Contact Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Here, we use epidemic simulations on empirical and simulated social networks to investigate the effectiveness of targeted testing of highly connected individuals to control an epidemic when testing capacity and social network information is limited. We simulate SARS-CoV-2 outbreaks on two close-contact social networks derived from social and spatial movement data on individuals living in rural communities in the Sambava district of the SAVA region in northeastern Madagascar (19). We then compare the effectiveness of testing strategies that target testing based on social connectivity to those that test randomly using the full knowledge of the social network, evaluating the time needed to control the epidemic, the total infection burden, and the number of tests needed.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging social network topology could improve the efficiency of SARS-CoV-2 epidemic control strategies in resource-limited contexts

Evans

Ramiadantsoa

Garchitorena

et al. 2022

Preprint

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BackgroundSuperspreading infections play an important role in the SARS-CoV-2 pandemic. Superspreading is caused primarily by heterogeneity in social contact rates, and therefore represents an opportunity for targeting surveillance and control via consideration of social network topologies, particularly in resource-limited settings. Yet, it remains unclear how to implement such surveillance and control, espeically when network data is unavailable.MethodsWe evaluated the efficiency of a testing strategy that targeted potential superspreading individuals based on their degree centrality on a social network compared to a random testing strategy in the context of low testing capacity. We simulated SARS-CoV-2 dynamics on two contact networks from rural Madagascar and measured the epidemic duration, infection burden, and tests needed to end the epidemics. In addition, we examined the robustness of this approach when individuals’ true degree centralities were unknown and were instead estimated via readily-available socio-demographic variables.FindingsTargeted testing of potential superspreaders reduced the infection burden by 40-63% at low testing capacities, while requiring between 45-78% fewer tests compared to random testing. Further, targeted testing remained more efficient when the true network topology was unknown and prioritization was based on socio-demographic characteristics.InterpretationIncorporating social network topology into epidemic control strategies is an effective public health strategy for health systems suffering from low testing capacity and can be implemented via socio-demographic proxies when social networks are unknown.FundingThis research was funded by the Agence Nationale de la Recherche, a NIH-SSF-NIFA Ecology and Evolution of Infectious Disease Award (No. 1R01-TW011493-01), and a Duke University Provost’s Collaboratory grant.

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Deriving spatially explicit direct and indirect interaction networks from animal movement data

Yang

Wilber

Manlove

et al. 2023

Ecology and Evolution

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Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily

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Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar

Cited by 11 publications

References 71 publications

On limitations of uniplex networks for modeling multiplex contagion

On limitations of uniplex networks for modeling multiplex contagion

Leveraging social network topology could improve the efficiency of SARS-CoV-2 epidemic control strategies in resource-limited contexts

Deriving spatially explicit direct and indirect interaction networks from animal movement data

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