Author(s): Anthony J. Masys
Keywords: Experimental futures analysis; Scenario planning; GIS; Predictive analytics; Disaster forensics;
Abstract: Heyman et al., (2015:1888) argues that, “the world is ill-prepared” to handle any “sustained and threatening public-health emergency”. Such public health emergencies stemming from infectious disease outbreaks is creating a serious threat to global health security. For example, climate change and extreme weather events threaten to alter and affect geographic areas pertaining to disease vulnerability, such as greater risks of mosquito-borne diseases (dengue, malaria, yellow fever and Zika). The emergence of these disease outbreaks and their influence globally has sparked a renewed attention on global health security. In the Chatham House report ‘Preparing for High Impact, Low Probability Events’, Lee et al (2012:vii) ‘…found that governments and businesses remain unprepared for such events’. Recent outbreaks characterize the ‘new normal’ and has unveiled major deficiencies in preparedness, response and recovery initiatives. For example, Ae. aegypti is one of the most significant mosquito species as it is capable of transmitting dengue fever, chikungunya, Zika, and yellow fever viruses. Understanding the emerging threat employing landscape real time epidemiological tools may ‘experimental futuring’ and scenario planning, this paper presents novel methods to predictively understand the processes by which species colonize and adapt to human habitats with a focus on the case of a virulent disease-vectoring arthropod such as Ae. aegypti. In this paper, we introduce real time ArcGIS machine learning (ML), spectral signatures in unmanned semi-Autonomous drone aircraft platform for controlling Ae. aegypti. mosquito habitats. The multivariate real time platform regressed the spatial risk of human exposure to Ae. aegypti pathogens to forecast unknown capture point georeferenceable geolocations of elevated risk. In so doing, the methodology described strengthens mitigation, preparedness, response and recovery through vulnerability analysis and predictive analytics.