A project from researchers in the MIT Engineering Systems Division (ESD) is among the 11 selected to receive public health research awards, totaling $2.7 million. These new awards are facilitated by the National Network of Public Health Institutes (NNPHI), with guidance from the National Coordinating Center for Public Health Services and Systems Research (NCC) housed at the University of Kentucky College of Public Health. Support for this research is provided by a grant from the Robert Wood Johnson Foundation. The $200,000 award for this project is administered over two years, from Septr 15, 2013 through Sept. 14, 2015.
The team comprises co-PIs Dr. Stan Finkelstein and Professor Richard Larson, as well as ESD PhD candidate Abigail Horn. Their project aims to develop a tool to better enable public health and emergency preparedness officials to identify the source of large-scale, multi-state outbreaks of foodborne illness.
The CDC estimates that each year in the U.S. 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths result from foodborne disease, at an estimated annual loss of $152 billion. Due to underreporting or unknown agents, these numbers are potentially much higher.
Based on the sporadic incidence of disease and the huge volume of food consumed the probability of any given food generating an outbreak is extremely low, however, low probability events do happen and may take on massive proportions.
“If recent trends continue,” says Larson, “including large-scale production practices and distribution over ever-larger distances, the consequences of these outbreaks will become even more dire.”
“The food production and delivery system involves extensive national and international networks whose structure and flows are always changing and not well documented. Contamination can occur anywhere in the system, such as on a ‘source node’ farm,” says Larson. “The issue is to identify and halt that source of contamination quickly, so as to minimize the number of consumers who may become ill due to the contamination. It’s a type of ‘detective search.’”
The team will build probabilistic network models of the production, transportation and distribution of selected food products, using data from federal and state health departments and industry sources, and derive algorithms to trace back to the sites where contamination is likely to have taken place. The key deliverable will be a planning tool enabling public health and emergency preparedness officials to make informed, trace-back policy decisions. The team will evaluate the utility of the tool against current and proposed methods used in outbreak investigations in order to determine cost-feasible recommendations for policy and practice.
The team comprises co-PIs Dr. Stan Finkelstein and Professor Richard Larson, as well as ESD PhD candidate Abigail Horn. Their project aims to develop a tool to better enable public health and emergency preparedness officials to identify the source of large-scale, multi-state outbreaks of foodborne illness.
The CDC estimates that each year in the U.S. 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths result from foodborne disease, at an estimated annual loss of $152 billion. Due to underreporting or unknown agents, these numbers are potentially much higher.
Based on the sporadic incidence of disease and the huge volume of food consumed the probability of any given food generating an outbreak is extremely low, however, low probability events do happen and may take on massive proportions.
“If recent trends continue,” says Larson, “including large-scale production practices and distribution over ever-larger distances, the consequences of these outbreaks will become even more dire.”
“The food production and delivery system involves extensive national and international networks whose structure and flows are always changing and not well documented. Contamination can occur anywhere in the system, such as on a ‘source node’ farm,” says Larson. “The issue is to identify and halt that source of contamination quickly, so as to minimize the number of consumers who may become ill due to the contamination. It’s a type of ‘detective search.’”
The team will build probabilistic network models of the production, transportation and distribution of selected food products, using data from federal and state health departments and industry sources, and derive algorithms to trace back to the sites where contamination is likely to have taken place. The key deliverable will be a planning tool enabling public health and emergency preparedness officials to make informed, trace-back policy decisions. The team will evaluate the utility of the tool against current and proposed methods used in outbreak investigations in order to determine cost-feasible recommendations for policy and practice.
