Efficient Surveillance and Temporal Calibration of Disease Response

A bstract Background Disease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, efficient surveillance is a prerequisite for an effective response. Methodology/Principal Findings We introduce the principle of epidemiological soundness and utilize it to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes efficient surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified efficiency thresholds. Conclusion We identify practically achievable, efficient surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved. Author Summary Efficient surveillance and effective response capabilities are pivotal to the prevention and control of the infectious diseases. The disease response is a set of reactive actions that follow the outcomes of the disease surveillance. Ergo, efficient surveillance is a perquisite for the deployment of an effective response. The quantification of the efficiency of a disease surveillance system largely depends on the epidemiological characteristics of the disease. In this paper, we introduce an approach that builds on these characteristics and measures the performance of a disease surveillance system through its impact on the incidence of the disease. Using this approach, we obtain quantitative (on a temporal scale) efficient surveillance thresholds, which if followed by a timely response, lead to a considerable reduction in the disease incidence. Furthermore, we show that these thresholds are practically achievable by identifying the obstacles that lead to less than efficient surveillance outcomes. Our approach can be applied to obtain guidelines on spatially, temporally and demographically targeted resource allocations for public awareness campaigns as well to improve diagnostic ability and turn-around times in treating doctors and pathology labs.