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1. A versatile web app for identifying the drivers of COVID-19 epidemics

   https://doi.org/10.1186/s12967-021-02736-2  

   Getz, Wayne M. ; Salter, Richard ; Luisa Vissat, Ludovica ; Horvitz, Nir ( March 2021 , Journal of Translational Medicine)

   No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. Thehttp://covid-webapp.numerusinc.com/web app presented here fills this gap.

   Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters.

   We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed.

   Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

    

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2. Linking Lyme disease ecology and epidemiology: reservoir host identity, not richness, determines tick infection and human disease in California

   https://doi.org/10.1088/1748-9326/ac9ece  

   MacDonald, Andrew J ; McComb, Sofie ; Sambado, Samantha ( November 2022 , Environmental Research Letters)

   Abstract Understanding the community ecology of vector-borne and zoonotic diseases, and how it may shift transmission risk as it responds to environmental change, has become a central focus in disease ecology. Yet, it has been challenging to link the ecology of disease with reported human incidence. Here, we bridge the gap between local-scale community ecology and large-scale disease epidemiology, drawing from a priori knowledge of tick-pathogen-host ecology to model spatially-explicit Lyme disease (LD) risk, and human Lyme disease incidence (LDI) in California. We first use a species distribution modeling approach to model disease risk with variables capturing climate, vegetation, and ecology of key reservoir host species, and host species richness. We then use our modeled disease risk to predict human disease incidence at the zip code level across California. Our results suggest the ecology of key reservoir hosts—particularly dusky-footed woodrats—is central to disease risk posed by ticks, but that host community richness is not strongly associated with tick infection. Predicted disease risk, which is most strongly influenced by the ecology of dusky-footed woodrats, in turn is a strong predictor of human LDI. This relationship holds in the Wildland-Urban Interface, but not in open access public lands, and is stronger in northern California than in the state as a whole. This suggests peridomestic exposure to infected ticks may be more important to LD epidemiology in California than recreational exposure, and underlines the importance of the community ecology of LD in determining human transmission risk throughout this LD endemic region of far western North America. More targeted tick and pathogen surveillance, coupled with studies of human and tick behavior could improve understanding of key risk factors and inform public health interventions. Moreover, longitudinal surveillance data could further improve forecasts of disease risk in response to global environmental change. 

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3. Variability in environmental persistence but not per capita transmission rates of the amphibian chytrid fungus leads to differences in host infection prevalence

   https://doi.org/10.1111/1365-2656.13612  

   Rumschlag, Samantha L. ; Roth, Sadie A. ; McMahon, Taegan A. ; Rohr, Jason R. ; Civitello, David J. ( November 2021 , Journal of Animal Ecology)

   Heterogeneities in infections among host populations may arise through differences in environmental conditions through two mechanisms. First, environmental conditions may alter host exposure to pathogens via effects on survival. Second, environmental conditions may alter host susceptibility, making infection more or less likely if contact between a host and pathogen occurs. Further, host susceptibility might be altered through acquired resistance, which hosts can develop, in some systems, through exposure to dead or decaying pathogens and their metabolites. Environmental conditions may alter the rates of pathogen decomposition, influencing the likelihood of hosts developing acquired resistance.

   The present study primarily tests how environmental context influences the relative contributions of pathogen survival and per capita transmission on host infection prevalence using the amphibian chytrid fungus (Batrachochytrium dendrobatidis; Bd) as a model system. Secondarily, we evaluate how environmental context influences the decomposition of Bd because previous studies have shown that dead Bd and its metabolites can illicit acquired resistance in hosts. We conducted Bd survival and infection experiments and then fit models to discern how Bd mortality, decomposition and per capita transmission rates vary among water sources [e.g. artificial spring water (ASW) or water from three ponds].

   We found that infection prevalence differed among water sources, which was driven by differences in mortality rates of Bd, rather than differences in per capita transmission rates. Bd mortality rates varied among pond water treatments and were lower in ASW compared to pond water.

   These results suggest that variation in Bd infection dynamics could be a function of environmental factors in waterbodies that result in differences in exposure of hosts to live Bd. In contrast to the persistence of live Bd, we found that the rates of decomposition of dead Bd did not vary among water sources, which may suggest that exposure of hosts to dead Bd or its metabolites might not commonly vary among nearby sites. Ultimately, a mechanistic understanding of the environmental dependence of free‐living pathogens could lead to a deeper understanding of the patterns of outbreak heterogeneity, which could inform surveillance and management strategies.

    

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4. Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental surveillance data

   https://doi.org/10.1073/pnas.1808798115  

   Brouwer, Andrew F. ; Eisenberg, Joseph N. S. ; Pomeroy, Connor D. ; Shulman, Lester M. ; Hindiyeh, Musa ; Manor, Yossi ; Grotto, Itamar ; Koopman, James S. ; Eisenberg, Marisa C. ( October 2018 , Proceedings of the National Academy of Sciences)

   Israel experienced an outbreak of wild poliovirus type 1 (WPV1) in 2013–2014, detected through environmental surveillance of the sewage system. No cases of acute flaccid paralysis were reported, and the epidemic subsided after a bivalent oral polio vaccination (bOPV) campaign. As we approach global eradication, polio will increasingly be detected only through environmental surveillance. We developed a framework to convert quantitative polymerase chain reaction (qPCR) cycle threshold data into scaled WPV1 and OPV1 concentrations for inference within a deterministic, compartmental infectious disease transmission model. We used this approach to estimate the epidemic curve and transmission dynamics, as well as assess alternate vaccination scenarios. Our analysis estimates the outbreak peaked in late June, much earlier than previous estimates derived from analysis of stool samples, although the exact epidemic trajectory remains uncertain. We estimate the basic reproduction number was 1.62 (95% CI 1.04–2.02). Model estimates indicate that 59% (95% CI 9–77%) of susceptible individuals (primarily children under 10 years old) were infected with WPV1 over a little more than six months, mostly before the vaccination campaign onset, and that the vaccination campaign averted 10% (95% CI 1–24%) of WPV1 infections. As we approach global polio eradication, environmental monitoring with qPCR can be used as a highly sensitive method to enhance disease surveillance. Our analytic approach brings public health relevance to environmental data that, if systematically collected, can guide eradication efforts.

    

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5. A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera

   https://doi.org/10.3390/tropicalmed6030147  

   Usmani, Moiz ; Brumfield, Kyle D. ; Jamal, Yusuf ; Huq, Anwar ; Colwell, Rita R. ; Jutla, Antarpreet ( September 2021 , Tropical Medicine and Infectious Disease)

   null (Ed.)

   Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal–oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction. 

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