Browsing by Author "Mordecai, Erin A."

Now showing 1 - 12 of 12
  • Thumbnail Image
    Age influences the thermal suitability of Plasmodium falciparum transmission in the Asian malaria vector Anopheles stephensi
    Miazgowicz, K. L.; Shocket, M. S.; Ryan, Sadie J.; Villena, O. C.; Hall, R. J.; Owen, J.; Adanlawo, T.; Balaji, K.; Johnson, Leah R.; Mordecai, Erin A.; Murdock, Courtney C. (2020-07-29)
    Models predicting disease transmission are vital tools for long-term planning of malaria reduction efforts, particularly for mitigating impacts of climate change. We compared temperature-dependent malaria transmission models when mosquito life-history traits were estimated from a truncated portion of the lifespan (a common practice) versus traits measured across the full lifespan. We conducted an experiment on adult femaleAnopheles stephensi, the Asian urban malaria mosquito, to generate daily per capita values for mortality, egg production and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated from truncated data versus observed lifetime values. Incorporating these temperature-trait relationships into an expression governing the thermal suitability of transmission, relativeR(0)(T), resulted in minor differences in the breadth of suitable temperatures forPlasmodium falciparumtransmission between the two models constructed from onlyAn. stephensitrait data. However, we found a substantial increase in thermal niche breadth compared with a previously published model consisting of trait data from multipleAnophelesmosquito species. Overall, this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based suitability predictions of transmission.
  • Thumbnail Image
    Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models
    Mordecai, Erin A.; Cohen, Jeremy M.; Evans, Michelle V.; Gudapati, Prithvi; Johnson, Leah R.; Lippi, Catherine A.; Miazgowicz, Kerri; Murdock, Courtney C.; Rohr, Jason R.; Ryan, Sadie J.; Savage, Van; Shocket, Marta S.; Stewart-Ibarra, Anna M.; Thomas, Matthew B.; Weikel, Daniel P. (PLOS, 2017-04)
    Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34 degrees C with maximal transmission occurring in a range from 26-29 degrees C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.
  • Thumbnail Image
    Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction
    Holcomb, Karen M.; Mathis, Sarabeth; Staples, J. Erin; Fischer, Marc; Barker, Christopher M.; Beard, Charles B.; Nett, Randall J.; Keyel, Alexander C.; Marcantonio, Matteo; Childs, Marissa L.; Gorris, Morgan E.; Rochlin, Ilia; Hamins-Puértolas, Marco; Ray, Evan L.; Uelmen, Johnny A.; DeFelice, Nicholas; Freedman, Andrew S.; Hollingsworth, Brandon D.; Das, Praachi; Osthus, Dave; Humphreys, John M.; Nova, Nicole; Mordecai, Erin A.; Cohnstaedt, Lee W.; Kirk, Devin; Kramer, Laura D.; Harris, Mallory J.; Kain, Morgan P.; Reed, Emily M. X.; Johansson, Michael A. (2023-01-12)
    Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).
  • Thumbnail Image
    Global expansion and redistribution of Aedes-borne virus transmission risk with climate change
    Ryan, Sadie J.; Carlson, Colin J.; Mordecai, Erin A.; Johnson, Leah R. (PLOS, 2019-03)
    Forecasting the impacts of climate change on Aedes-borne viruses, especially dengue, chikungunya, and Zika is a key component of public health preparedness. We apply an empirically parameterized model of viral transmission by the vectors Aedes aegypti and Ae. albopictus, as a function of temperature, to predict cumulative monthly global transmission risk in current climates, and compare them with projected risk in 2050 and 2080 based on general circulation models (GCMs). Our results show that if mosquito range shifts track optimal temperature ranges for transmission (21.3-34.0 degrees C for Ae. aegypti; 19.9-29.4 degrees C for Ae. albopictus), we can expect poleward shifts in Aedes-borne virus distributions. However, the differing thermal niches of the two vectors produce different patterns of shifts under climate change. More severe climate change scenarios produce larger population exposures to transmission by Ae. aegypti, but not by Ae. albopictus in the most extreme cases. Climate-driven risk of transmission from both mosquitoes will increase substantially, even in the short term, for most of Europe. In contrast, significant reductions in climate suitability are expected for Ae. albopictus, most noticeably in southeast Asia and west Africa. Within the next century, nearly a billion people are threatened with new exposure to virus transmission by both Aedes spp. in the worst-case scenario. As major net losses in year-round transmission risk are predicted for Ae. albopictus, we project a global shift towards more seasonal risk across regions. Many other complicating factors (like mosquito range limits and viral evolution) exist, but overall our results indicate that while climate change will lead to increased net and new exposures to Aedes-borne viruses, the most extreme increases in Ae. albopictus transmission are predicted to occur at intermediate climate change scenarios.
  • Thumbnail Image
    Global Health Impacts for Economic Models of Climate Change: A Systematic Review and Meta-Analysis
    Cromar, Kevin R.; Anenberg, Susan C.; Balmes, John R.; Fawcett, Allen A.; Ghazipura, Marya; Gohlke, Julia M.; Hashizume, Masahiro; Howard, Peter; Lavigne, Eric; Levy, Karen; Madrigano, Jaime; Martinich, Jeremy A.; Mordecai, Erin A.; Rice, Mary B.; Saha, Shubhayu; Scovronick, Noah C.; Sekercioglu, Fatih; Svendsen, Erik R.; Zaitchik, Benjamin F.; Ewart, Gary (American Thoracic Society, 2022-07)
    Rationale: Avoiding excess health damages attributable to climate change is a primary motivator for policy interventions to reduce greenhouse gas emissions. However, the health benefits of climate mitigation, as included in the policy assessment process, have been estimated without much input from health experts. Objectives: In accordance with recommendations from the National Academies in a 2017 report on approaches to update the social cost of greenhouse gases (SC-GHG), an expert panel of 26 health researchers and climate economists gathered for a virtual technical workshop in May 2021 to conduct a systematic review and meta-analysis and recommend improvements to the estimation of health impacts in economic-climate models. Methods: Regionally resolved effect estimates of unit increases in temperature on net all-cause mortality risk were generated through random-effects pooling of studies identified through a systematic review. Results: Effect estimates and associated uncertainties varied by global region, but net increases in mortality risk associated with increased average annual temperatures (ranging from 0.1% to 1.1% per 1 degrees C) were estimated for all global regions. Key recommendations for the development and utilization of health damage modules were provided by the expert panel and included the following: not relying on individual methodologies in estimating health damages; incorporating a broader range of cause-specific mortality impacts; improving the climate parameters available in economic models; accounting for socioeconomic trajectories and adaptation factors when estimating health damages; and carefully considering how air pollution impacts should be incorporated in economic-climate models. Conclusions: This work provides an example of how subject-matter experts can work alongside climate economists in making continued improvements to SC-GHG estimates.
  • Thumbnail Image
    Mathematical models are a powerful method to understand and control the spread of Huanglongbing
    Taylor, Rachel A.; Mordecai, Erin A.; Gilligan, Christopher A.; Rohr, Jason R.; Johnson, Leah R. (PeerJ, 2016-11-03)
    Huanglongbing (HLB), or citrus greening, is a global citrus disease occurring in almost all citrus growing regions. It causes substantial economic burdens to individual growers, citrus industries and governments. Successful management strategies to reduce disease burden are desperately needed but with so many possible interventions and combinations thereof it is difficult to know which are worthwhile or cost-effective. We review how mathematical models have yielded useful insights into controlling disease spread for other vector-borne plant diseases, and the small number of mathematical models of HLB. We adapt a malaria model to HLB, by including temperature-dependent psyllid traits, “flushing” of trees, and economic costs, to show how models can be used to highlight the parameters that require more data collection or that should be targeted for intervention. We analyze the most common intervention strategy, insecticide spraying, to determine the most cost-effective spraying strategy. We find that fecundity and feeding rate of the vector require more experimental data collection, for wider temperatures ranges. Also, the best strategy for insecticide intervention is to spray for more days rather than pay extra for a more efficient spray. We conclude that mathematical models are able to provide useful recommendations for managing HLB spread.
  • Thumbnail Image
    An open challenge to advance probabilistic forecasting for dengue epidemics
    Johansson, Michael A.; Apfeldorf, Karyn M.; Dobson, Scott; Devita, Jason; Buczak, Anna L.; Baugher, Benjamin; Moniz, Linda J.; Bagley, Thomas; Babin, Steven M.; Guven, Erhan; Yamana, Teresa K.; Shaman, Jeffrey; Moschou, Terry; Lothian, Nick; Lane, Aaron; Osborne, Grant; Jiang, Gao; Brooks, Logan C.; Farrow, David C.; Hyun, Sangwon; Tibshirani, Ryan J.; Rosenfeld, Roni; Lessler, Justin; Reich, Nicholas G.; Cummings, Derek AT T.; Lauer, Stephen A.; Moore, Sean M.; Clapham, Hannah E.; Lowe, Rachel; Bailey, Trevor C.; Garcia-Diez, Markel; Carvalho, Marilia Sa; Rodo, Xavier; Sardar, Tridip; Paul, Richard; Ray, Evan L.; Sakrejda, Krzysztof; Brown, Alexandria C.; Meng, Xi; Osoba, Osonde; Vardavas, Raffaele; Manheim, David; Moore, Melinda; Rao, Dhananjai M.; Porco, Travis C.; Ackley, Sarah; Liu, Fengchen; Worden, Lee; Convertino, Matteo; Liu, Yang; Reddy, Abraham; Ortiz, Eloy; Rivero, Jorge; Brito, Humberto; Juarrero, Alicia; Johnson, Leah R.; Gramacy, Robert B.; Cohen, Jeremy M.; Mordecai, Erin A.; Murdock, Courtney C.; Rohr, Jason R.; Ryan, Sadie J.; Stewart-Ibarra, Anna M.; Weikel, Daniel P.; Jutla, Antarpreet; Khan, Rakibul; Poultney, Marissa; Colwell, Rita R.; Rivera-Garcia, Brenda; Barker, Christopher M.; Bell, Jesse E.; Biggerstaff, Matthew; Swerdlow, David; Mier-y-Teran-Romero, Luis; Forshey, Brett M.; Trtanj, Juli; Asher, Jason; Clay, Matt; Margolis, Harold S.; Hebbeler, Andrew M.; George, Dylan; Chretien, Jean-Paul (National Academy of Sciences, 2019-11-26)
    A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.
  • Thumbnail Image
    The Role of Vector Trait Variation in Vector-Borne Disease Dynamics
    Cator, Lauren J.; Johnson, Leah R.; Mordecai, Erin A.; El Moustaid, Fadoua; Smallwood, Thomas R. C.; LaDeau, Shannon L.; Johansson, Michael A.; Hudson, Peter J.; Boots, Michael; Thomas, Matthew B.; Power, Alison G.; Pawar, Samraat (2020-07-10)
    Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.
  • Thumbnail Image
    Thermal biology of mosquito-borne disease
    Mordecai, Erin A.; Caldwell, Jamie M.; Grossman, Marissa K.; Lippi, Catherine A.; Johnson, Leah R.; Neira, Marco; Rohr, Jason R.; Ryan, Sadie J.; Savage, Van; Shocket, Marta S.; Sippy, Rachel; Ibarra, Anna M. Stewart; Thomas, Matthew B.; Villena, Oswaldo (Wiley, 2019-07-08)
    Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species – including globally important diseases like malaria, dengue, and Zika – synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23–29ºC and declining to zero below 9–23ºC and above 32–38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25–26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.
  • Thumbnail Image
    Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 degrees C and 26 degrees C
    Shocket, Marta S.; Verwillow, Anna B.; Numazu, Mailo G.; Slamani, Hani; Cohen, Jeremy M.; El Moustaid, Fadoua; Rohr, Jason R.; Johnson, Leah R.; Mordecai, Erin A. (2020-09-15)
    The temperature-dependence of many important mosquito-borne diseases has never been quantified. These relationships are critical for understanding current distributions and predicting future shifts from climate change. We used trait-based models to characterize temperature-dependent transmission of 10 vector-pathogen pairs of mosquitoes (Culex pipiens, Cx. quinquefascsiatus, Cx. tarsalis, and others) and viruses (West Nile, Eastern and Western Equine Encephalitis, St. Louis Encephalitis, Sindbis, and Rift Valley Fever viruses), most with substantial transmission in temperate regions. Transmission is optimized at intermediate temperatures (23-26 degrees C) and often has wider thermal breadths (due to cooler lower thermal limits) compared to pathogens with predominately tropical distributions (in previous studies). The incidence of human West Nile virus cases across US counties responded unimodally to average summer temperature and peaked at 24 degrees C, matching model-predicted optima (24-25 degrees C). Climate warming will likely shift transmission of these diseases, increasing it in cooler locations while decreasing it in warmer locations.
  • Thumbnail Image
    Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach
    Johnson, Leah R.; Ben-Horin, Tal; Lafferty, Kevin D.; McNally, Amy; Mordecai, Erin A.; Paaijmans, Krijn P.; Pawar, Samraat; Ryan, Sadie J. (Ecological Society of America, 2015)
    Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number R₀. However, understanding the mechanisms linking R₀ and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this, we show how a Bayesian approach can help identify critical uncertainties in components of R₀ and how this uncertainty is propagated into the estimate of R₀. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15°C to 25°C; fecundity across all temperatures, but especially ~25–32°C; mortality from 20°C to 30°C; parasite development rate at ~15–16°C and again at ~33–35°C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of R₀. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.
  • Thumbnail Image
    Warming temperatures could expose more than 1.3 billion new people to Zika virus risk by 2050
    Ryan, Sadie J.; Carlson, Colin J.; Tesla, Blanka; Bonds, Matthew H.; Ngonghala, Calistus N.; Mordecai, Erin A.; Johnson, Leah R.; Murdock, Courtney C. (2020-10-09)
    In the aftermath of the 2015 pandemic of Zika virus (ZIKV), concerns over links between climate change and emerging arboviruses have become more pressing. Given the potential that much of the world might remain at risk from the virus, we used a previously established temperature-dependent transmission model for ZIKV to project climate change impacts on transmission suitability risk by mid-century (a generation into the future). Based on these model predictions, in the worst-case scenario, over 1.3 billion new people could face suitable transmission temperatures for ZIKV by 2050. The next generation will face substantially increased ZIKV transmission temperature suitability in North America and Europe, where naive populations might be particularly vulnerable. Mitigating climate change even to moderate emissions scenarios could significantly reduce global expansion of climates suitable for ZIKV transmission, potentially protecting around 200 million people. Given these suitability risk projections, we suggest an increased priority on research establishing the immune history of vulnerable populations, modeling when and where the next ZIKV outbreak might occur, evaluating the efficacy of conventional and novel intervention measures, and increasing surveillance efforts to prevent further expansion of ZIKV.