Genotypic variation in parasite avoidance behaviour and other mechanistic, nonlinear components of transmission

Traditional epidemiological models assume that transmission increases proportionally to the density of parasites. However, empirical data frequently contradict this assumption. General yet mechanistic models can explain why transmission depends nonlinearly on parasite density and thereby identify potential defensive strategies of hosts. For example, hosts could decrease their exposure rates at higher parasite densities (via behavioural avoidance) or decrease their per-parasite susceptibility when encountering more parasites (e.g. via stronger immune responses). To illustrate, we fitted mechanistic transmission models to 19 genotypes of Daphnia dentifera hosts over gradients of the trophically acquired parasite, Metschnikowia bicuspidata . Exposure rate (foraging, F ) frequently decreased with parasite density ( Z ), and per-parasite susceptibility ( U ) frequently decreased with parasite encounters ( F × Z ). Consequently, infection rates ( F × U × Z ) often peaked at intermediate parasite densities. Moreover, host genotypes varied substantially in these responses. Exposure rates remained constant for some genotypes but decreased sensitively with parasite density for others (up to 78%). Furthermore, genotypes with more sensitive foraging/exposure also foraged faster in the absence of parasites (suggesting ‘fast and sensitive’ versus ‘slow and steady’ strategies). These relationships suggest that high densities of parasites can inhibit transmission by decreasing exposure rates and/or per-parasite susceptibility, and identify several intriguing axes for the evolution of host defence.

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Understanding mosquito host-choice behaviour: a new and low-cost method of identifying the sex of human hosts from mosquito blood meals

Parasites & Vectors ◽

10.1186/s13071-021-04577-w ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Fiona Teltscher ◽

Sophie Bouvaine ◽

Gabriella Gibson ◽

Paul Dyer ◽

Jennifer Guest ◽

...

Keyword(s):

Blood Meal ◽

Host Species ◽

Disease Transmission ◽

Low Cost ◽

Host Choice ◽

Epidemiological Models ◽

Choice Behaviour ◽

Vector Borne ◽

Transmission Models ◽

Blood Meals

Abstract Background Mosquito-borne diseases are a global health problem, causing hundreds of thousands of deaths per year. Pathogens are transmitted by mosquitoes feeding on the blood of an infected host and then feeding on a new host. Monitoring mosquito host-choice behaviour can help in many aspects of vector-borne disease control. Currently, it is possible to determine the host species and an individual human host from the blood meal of a mosquito by using genotyping to match the blood profile of local inhabitants. Epidemiological models generally assume that mosquito biting behaviour is random; however, numerous studies have shown that certain characteristics, e.g. genetic makeup and skin microbiota, make some individuals more attractive to mosquitoes than others. Analysing blood meals and illuminating host-choice behaviour will help re-evaluate and optimise disease transmission models. Methods We describe a new blood meal assay that identifies the sex of the person that a mosquito has bitten. The amelogenin locus (AMEL), a sex marker located on both X and Y chromosomes, was amplified by polymerase chain reaction in DNA extracted from blood-fed Aedes aegypti and Anopheles coluzzii. Results AMEL could be successfully amplified up to 24 h after a blood meal in 100% of An. coluzzii and 96.6% of Ae. aegypti, revealing the sex of humans that were fed on by individual mosquitoes. Conclusions The method described here, developed using mosquitoes fed on volunteers, can be applied to field-caught mosquitoes to determine the host species and the biological sex of human hosts on which they have blood fed. Two important vector species were tested successfully in our laboratory experiments, demonstrating the potential of this technique to improve epidemiological models of vector-borne diseases. This viable and low-cost approach has the capacity to improve our understanding of vector-borne disease transmission, specifically gender differences in exposure and attractiveness to mosquitoes. The data gathered from field studies using our method can be used to shape new transmission models and aid in the implementation of more effective and targeted vector control strategies by enabling a better understanding of the drivers of vector-host interactions.

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From abstract to ideal–The limits of models. A reply to Pawson’s ‘boxed in by models’

Evaluation ◽

10.1177/13563890211007505 ◽

2021 ◽

pp. 135638902110075

Author(s):

Axel Kaehne

Keyword(s):

Political Theory ◽

Epidemiological Models ◽

Infection Rates ◽

Social Scientists ◽

Complex Adaptive ◽

The Difference ◽

The Government ◽

Adaptive Nature ◽

Onora O’Neill ◽

Better Than

Pawson’s article raises the important question of what constitutes good and bad modelling during a pandemic. His article makes the case for more involvement of social scientists to capture the complex adaptive nature of governmental policy. While articulating a welcome critique of epidemiological models, his article fails to recognise that all model use simplifications which make some models better than others. I will suggest a useful way of differentiating between good and bad, useful and less useful, models based on the difference between idealisation and abstraction, concepts I borrow from Onora O’Neill and political theory. They allow us to apply a more nuanced criticality to the current models used by the government. Refining our critique of the government’s COVID response is important since we need to account for the fact that current government responses to the pandemic, while open to criticism, have had some effect in reducing infection rates.

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Parasite avoidance behaviours in aquatic environments

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0202 ◽

2018 ◽

Vol 373 (1751) ◽

pp. 20170202 ◽

Cited By ~ 18

Author(s):

Donald C. Behringer ◽

Anssi Karvonen ◽

Jamie Bojko

Keyword(s):

Disease Ecology ◽

Aquatic Systems ◽

Aquatic Environments ◽

Defence Mechanism ◽

Negative Effects ◽

Behavioural Avoidance ◽

Trade Offs ◽

Heavy Burden ◽

Freshwater Environments ◽

Parasite Avoidance

Parasites, including macroparasites, protists, fungi, bacteria and viruses, can impose a heavy burden upon host animals. However, hosts are not without defences. One aspect of host defence, behavioural avoidance, has been studied in the terrestrial realm for over 50 years, but was first reported from the aquatic environment approximately 20 years ago. Evidence has mounted on the importance of parasite avoidance behaviours and it is increasingly apparent that there are core similarities in the function and benefit of this defence mechanism between terrestrial and aquatic systems. However, there are also stark differences driven by the unique biotic and abiotic characteristics of terrestrial and aquatic (marine and freshwater) environments. Here, we review avoidance behaviours in a comparative framework and highlight the characteristics of each environment that drive differences in the suite of mechanisms and cues that animals use to avoid parasites. We then explore trade-offs, potential negative effects of avoidance behaviour and the influence of human activities on avoidance behaviours. We conclude that avoidance behaviours are understudied in aquatic environments but can have significant implications for disease ecology and epidemiology, especially considering the accelerating emergence and re-emergence of parasites. This article is part of the Theo Murphy meeting issue ‘Evolution of pathogen and parasite avoidance behaviours'.

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A Comparison of Five Epidemiological Models for Transmission of SARS-CoV-2 in India

10.21203/rs.3.rs-141247/v1 ◽

2021 ◽

Author(s):

Soumik Purkayastha ◽

Rupam Bhattacharyya ◽

Ritwik Bhaduri ◽

Ritoban Kundu ◽

Xuelin Gu ◽

...

Keyword(s):

Disease Transmission ◽

Correlation Coefficients ◽

Simulation Studies ◽

Epidemiological Models ◽

Baseline Model ◽

Bayesian Hierarchical ◽

Active Case ◽

Pandemic Planning ◽

Appreciable Variation ◽

Transmission Models

Abstract BackgroundMany popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). MethodsUsing COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. ResultsFor active case counts, SMAPE values are 0.72 (SEIR-fansy) and 33.83 (eSIR). For cumulative case counts, SMAPE values are 1.76 (baseline) 23. (eSIR), 2.07 (SAPHIRE) and 3.20 (SEIR-fansy). For cumulative death counts, the SMAPE values are 7.13 (SEIR-fansy) and 26.30 (eSIR). For cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported COVID-counts agree. Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors as of June 30 and note that the SEIR-fansy model reports the highest underreporting factor for active cases (6.10) and cumulative deaths (3.62), while the SAPHIRE model reports the highest underreporting factor for cumulative cases (27.79).ConclusionsIn this comparative paper we describe five different models used to study full disease transmission of the SARS-Cov-2 disease transmission in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. Prediction of daily active number of cases does show appreciable variation across models. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases. The degree of under-reporting has been a major concern in India.

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COVID-19 pandemic-related lockdown: response time is more important than its strictness

10.1101/2020.06.11.20128520 ◽

2020 ◽

Author(s):

Gil Loewenthal ◽

Shiran Abadi ◽

Oren Avram ◽

Keren Halabi ◽

Noa Ecker ◽

...

Keyword(s):

Response Time ◽

Mortality Rates ◽

Data Driven ◽

Epidemiological Models ◽

Infection Rates ◽

Social Distancing ◽

Expected Time ◽

Daily Mobility ◽

Rapid Spread ◽

Country Specific

AbstractThe rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of mobility characteristics which were crossed with observed mortality rates to show that: (1) the time at which social distancing was initiated is of utmost importance and explains 62% of the number of deaths, while the lockdown strictness or its duration are not as informative; (2) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (3) the expected time from infection to fatality is 25.75 days and significantly varies among countries.

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