Evaluating the Cost-effectiveness of Different Surveillance Approaches for Specific Diseases

The article compares the cost-effectiveness analysis (CEA) of different surveillance systems applied in the field of public health to keep a track of infectious diseases and other health risks. Since available resources in most cases are scarce to address the public health, a cost-effective review of the surveillance systems is essential to the decision-making and effective distribution of resources. This paper aims to determine the relative cost-effectiveness of various surveillance strategies, e.g., syndromic surveillance, active surveillance, and genomic surveillance, in different types of diseases, and what are the best practices in implementing cost-effective surveillance strategies in resource-limited contexts. Methods and Materials: A literature search was performed based on the literature concerning the cost-effectiveness of public health surveillance systems with a priority to those publications published during the past decade. Some of the types of various diseases that were analyzed encompassed acute respiratory infections, vaccine-preventable diseases, vector-borne diseases, and antimicrobial resistance. Each study provided a combination of cost and effectiveness data, where incremental cost-effectiveness ratios (ICERs) and sensitivity analysis were mainly needed to estimate uncertainty in model assumptions. Findings: The findings suggest that hybrid surveillance systems that combine information related to a variety of sources (e.g., human, environmental and animal health) are the most affordable options in the high-resource and low-resource contexts. The cost-effectiveness of various surveillance strategies depends, however, on the disease, healthcare infrastructure of a particular country, and available technologies. A real-time surveillance system though it was initially costly was very effective in controlling outbreaks through quick response. Future Work: Future studies should be aimed at creating more unified surveillance models where real-time data is combined with predictive analytics to enhance timeliness and accuracy of outbreak detection. Besides this, the economic effects of hybrid systems and machine-learning implementation to achieve predictive surveillance should also be investigated in future studies to overcome the challenges arising due to the global health threats.