7/11/2019 Objectives • Understand … • the interactions between humans, environment, and Clima te c ha ng e pathogens • how temperature effects mosquito physiology & the e c o lo g y o f • how climate data can be used to predict vector ‐ borne ve c to r-b o rne disease dise ase s De sire e L a Be a ud, MD, MS Asso c ia te Pro fe sso r, Sta nfo rd Unive rsity 1 2 Vector ‐ Borne Diseases: What are Arboviruses? • Ar thropod ‐ bo rne viruses • Require a blood sucking arthropod to complete the life cycle • Often zoonotic – animal to human transmission • At least 500 viruses • Diverse: 8 viral families • Togaviridae, Flaviviridae, and Bunyaviridae 3 4 What is Climate Change? Extreme Weather Events Are More Frequent Burning fossil fuels leads to increased temperature: • Sea level rise • Ice mass loss • Shifts in flower/plant blooming • Alteration of mosquito habitat • Extreme weather events • Warming temperatures 5 6 1
7/11/2019 Major Emerging and Reemerging Disease Emergence: Influences of Modern Life Infectious Disease Outbreaks, 2002 ‐ 2015 Urbanization Climate Change Land Use Change: Natural Disasters Deforestation/Reforestation, Extreme Weather Land Reclamation, Irrigation Events Projects Reduced Capacity to Military Activities/War Sustain Clean Water Reduced/Ineffective Vector Control Increased Transportation National Academy of Medicine, 2016 7 8 Interactions between climate change and infectious diseases are complex and poorly understood • Chikungunya is spread by a day biting mosquito ( Aedes aegypti ) which prefers to breed in man ‐ made plastic containers • 2004 ‐ 5 Kenyan chikungunya outbreak linked to drought • Unusually dry, warm conditions preceded the outbreaks, including the driest since 1998 for some of the coastal regions • Infrequent replenishment of domestic water stores and elevated temperatures may have facilitated transmission • Underscores the need for safe water storage during drought relief operations Nic o le No va , fro m Sho c ke t e t a l., in re vie w Chretien et al. AJTMH 2007 9 10 Nic o le No va , Nic o le No va , fro m Sho c ke t e t a l., in re vie w fro m Sho c ke t e t a l., in re vie w 11 12 2
7/11/2019 Many mo de ls How will climate change affect “Wa rme r is sic ke r” vector ‐ borne disease transmission? E pste in 2000 13 14 Many mo de ls Glo b a l c ha ng e & tra nsmissio n suita b ility Mo sq uito physio lo g y Re lative R 0 25 30 15 20 35 T e mpe rature (C) (De ll e t al . 2011, T ho mas & Blanfo rd 2003, and many o the rs) 15 16 Our approach Our approach F it physio lo g ic al F it physio lo g ic al re spo nse s with data re spo nse s with data Calc ulate R 0 vs. te mpe rature symme tric & a symme tric , symme tric & a symme tric , line a r (fo r c o mpa riso n) line a r (fo r c o mpa riso n) 17 18 3
7/11/2019 Our approach Our approach F it physio lo g ic al F it physio lo g ic al re spo nse s with data re spo nse s with data Calc ulate R 0 vs. Calc ulate R 0 vs. te mpe rature te mpe rature symme tric & a symme tric , symme tric & a symme tric , Validate Validate line a r (fo r c o mpa riso n) line a r (fo r c o mpa riso n) with with fie ld data fie ld data Pro je c t unde r future c limate 19 20 Temperature and Malaria Optimal temperature range for malaria lower than previously predicted Biting Rate ● Mosquito Infection Rate ● Transmission competence ● Parasite Development ● Temperature Rate Dependent Adult mosquito mortality ● Processes rate Daily egg laying rate ● Egg to adult Survival ● Mosquito developmental ● Mordecai, Erin A., et al. "Optimal temperature for malaria transmission is dramatically lower rate than previously predicted." Ecology letters 16.1 (2013): 22 ‐ 30. 21 22 Four Outpa Fo Outpatien ient Si Sites in in Ke Kenya nya Our Our st study: udy: Is the temperature effect predicted by this ecological model applicable to clinical malaria incidence? Temperature, Rainfall and Humidity measured daily Children with Undifferentiated Febrile Illness n=5,833 n=5,8 23 24 4
7/11/2019 Effect of 30 ‐ day Mean Malaria Smear Positivity and Temperature Ranges Temperature on at the Four Sites Smear Positivity at Four Clinical Sites 83% 50% *controlling for rainfall, bednet use, sex, age, socioeconomic status 58% 59% Shah MM et al.. Malaria smear positivity among Kenyan children peaks at intermediate temperatures as predicted by ecological models. In print Parasites and Vectors . 25 26 Malaria Smear Positivity plotted alongside Relative R o Climate change will shift bur burden of ma malaria laria at Four Clinical Sites Shah MM et al.. Malaria smear positivity among Kenyan children Ryan, Sadie J., et al. "Mapping physiological suitability limits for peaks at intermediate temperatures as predicted by ecological malaria in Africa under climate change." Vector ‐ Borne and models. In print Parasites and Vectors . Zoonotic Diseases 15.12 (2015): 718 ‐ 725. 27 28 Dengue, Chikungunya, and Zika Aedes aegypti and DENV Ae de s ae gypti Ae de s albo pic tus CDC De ng ue Ma p Aedes albopictus and DENV Mo rde c ai e t al., 2017 PL o S NT D 29 30 5
7/11/2019 Compared to previous models R 0 for DENV, CHIKV, ZIKV validate d Mo rde c ai e t al., 2017 PL o S NT D Mo rde c ai e t al., 2017 PL o S NT D 31 32 Four Outpa Fo Outpatien ient Si Sites in in Ke Kenya nya Non ‐ Malarial Fever and Temperature Temperature, Rainfall and Humidity measured daily Children with Undifferentiated Febrile Illness n=5,833 n=5,8 33 34 WHEN AND WHERE IS CLIMATE SUITABLE FOR TRANSMISSION? WHEN AND WHERE IS CLIMATE SUITABLE FOR TRANSMISSION? Current climate conditions Current climate conditions Aedes aegypti Aedes aegypti 2050 climate conditions Rya n e t a l Rya n e t a l Bio Rxiv Bio Rxiv 35 36 6
7/11/2019 Reproductive Number Curves for Malaria and Dengue Virus Clima te c ha ng e ma y drive a shift fro m malar ia to dengue in Afric a Curre nt 2050 2080 Malaria De ng ue Mordecai EM, Sadie R, Caldwell J, Shah MM, LaBeaud AD. Climate change could shift disease burden from malaria to arboviruses in Africa. Nature Climate Mo rde c ai, Ryan, Caldwe ll, Shah, L aBe aud, in re vie w Nature Climate Chang e Change. Under review. 37 38 Will climate change shift disease burden across the world? Challenges FOR the world • Non ‐ immune populations • Widespread competent mosquito vectors • No rapid local testing currently • Limited physician knowledge and clinical suspicion • Poor diagnostics • No treatments or vaccines 39 40 Mitigating Arboviral Diseases Innovative Approaches • Infrastructure • Sustained mosquito control efforts are important to prevent outbreaks • Modelling temperature • Address research gaps patterns to predict • Better diagnostics, Vaccines, Targeted therapeutics mosquito breeding • Disease tracking • Develop optimal scientific approaches to understanding the many factors associated with climate change and infectious diseases • Improve the prediction of the spatial–temporal process of climate change and the associated shifts in infectious diseases at various spatial and temporal scales • Establish locally effective early warning systems for the health effects of evolving climate change Shocket et al, eLIFE, 2018 41 42 7
7/11/2019 Mo sq uito pre dic tio ns vs. o b se rva tio ns Model Inputs Model Outputs Mean temperature ( ° C) 32 30 16k Modeled mosquitoes 27 Modeled mosquitoes Observed mosquitoes 22 25 Relative humidity 90 20 60 10k 30 Modeled disease 15 cases Rainfall (mm) 80 10 20 4k 5 2013 2014 2015 2016 2017 Model Data 2014 2015 2016 2017 2018 2019 43 44 Forecasting vector abundance using climate data El Niño year (wet) La Niña year (dry) Normal year 2015 2016 2017 2018 2015 2016 2017 2018 45 46 Huma n dise a se pre dic tio ns vs. o b se rva tio ns Huma n Be ha vio r Ma tte rs! 47 48 8
7/11/2019 Co nc lusio ns • Clima Climate is important for determining where and when conditions are Thank you suitable for transmission for your • Climate change will exert a nuanced effect on vector ‐ borne disease transmission depending on location attention! • Climate change may promote a shift from malaria to arboviruses in many parts of sub ‐ Saharan Africa • Mo Mosquit uito micr microh ohabit itats and human human beha behavior vior can contribute to variation in transmission within sites with similar temperatures 49 50 9
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