How many suffice? A computational framework for sizing sentinel surveillance networks

Abstract

BackgroundData from surveillance networks help epidemiologists and public health officials detect emerging diseases, conduct outbreak investigations, manage epidemics, and better understand the mechanics of a particular disease. Surveillance networks are used to determine outbreak intensity (i.e., disease burden) and outbreak timing (i.e., the start, peak, and end of the epidemic), as well as outbreak location. Networks can be tuned to preferentially perform these tasks. Given that resources are limited, careful site selection can save costs while minimizing performance loss.MethodsWe study three different site placement algorithms: two algorithms based on the maximal coverage model and one based on the K-median model. The maximal coverage model chooses sites that maximize the total number of people within a specified distance of a site. The K-median model minimizes the sum of the distances from each individual to the individual’s nearest site. Using a ground truth dataset consisting of two million de-identified Medicaid billing records representing eight complete influenza seasons and an evaluation function based on the Huff spatial interaction model, we empirically compare networks against the existing Iowa Department of Public Health influenza-like illness network by simulating the spread of influenza across the state of Iowa.ResultsWe show that it is possible to design a network that achieves outbreak intensity performance identical to the status quo network using two fewer sites. We also show that if outbreak timing detection is of primary interest, it is actually possible to create a network that matches the existing network’s performance using 59% fewer sites.ConclusionsBy simulating the spread of influenza across the state of Iowa, we show that our methods are capable of designing networks that perform better than the status quo in terms of both outbreak intensity and timing. Additionally, our results suggest that network size may only play a minimal role in outbreak timing detection. Finally, we show that it may be possible to reduce the size of a surveillance system without affecting the quality of surveillance information produced.

Socioeconomic bias in influenza surveillance

“…Ironically, surveillance systems seem to neglect communities most in need of intervention. New methods for designing and optimizing disease data collection have focused on state-level coverage [55][56][57][58][59] or assumed that risk was evenly spread across well-mixed populations [60], but could be adapted to identify data sources that remedy critical gaps or biases.…”

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

“…Artificial Intelligence includes a wide range of methods and algorithms that work based on machine intelligence and has many applications in various fields of science [40], including fuzzy logic theory and application [41][42][43][44][45][46], artificial intelligence techniques and sociology [47][48][49], risk assessment and hazard identification [50,51], machine learning [52][53][54][55][56][57], and meta-heuristic algorithms and clustering techniques [58][59][60][61][62]. The group method of data handling (GMDH) type of neural network is one of these algorithms that was proposed by Ivakhnenko [63].…”

Tracking Cholera through Surveillance of Oral Rehydration Solution Sales at Pharmacies: Insights from Urban Bangladesh

“…We then enrolled 50 pharmacies, out of 124 due to cost and human resource constraints, with equal probability, replacing those who declined to participate with the next on the list ( Fig 1 ). We used uniform random selection, as opposed to a more advanced surveillance site selection [ 13 , 14 ] technique, as no prior sales data, nor high-resolution population data, were available. At each pharmacy we identified the employees primarily responsible for diarrhea-related sales and trained each on the use of paper forms and phone reporting systems.…”