Comparing national and global data collection systems for reporting, outbreaks of H5N1 HPAI

Spatio-Temporal Data Comparisons for Global Highly Pathogenic Avian Influenza (HPAI) H5N1 Outbreaks

“…Clearly, underreporting of global HPAI H5N1 outbreaks is inevitable, and H5N1, as is the case with other animal disease, is commonly underreported across all types of production systems, particularly in the commercial poultry sectors in many countries [25], [27], [29]. Several reasons may result in this underreporting: 1) Active surveillance of H5N1 in countries where the disease is endemic is always low or absent, which makes the national surveillance systems do not have enough capacity to capture the whole profile of HPAI H5N1 outbreaks [25]; 2) Unwillingness to report H5N1 outbreaks due to reasons such as political pretexts [30]; 3) HPAI H5N1 is not a disease with high priority in some regions, so awareness of the need and importance to report outbreaks is lacking [31]; and 4) Stakeholders do not like to report H5N1 outbreaks because of inadequate compensation for culled animals if reported [32].…”

“…Several reasons may result in this underreporting: 1) Active surveillance of H5N1 in countries where the disease is endemic is always low or absent, which makes the national surveillance systems do not have enough capacity to capture the whole profile of HPAI H5N1 outbreaks [25]; 2) Unwillingness to report H5N1 outbreaks due to reasons such as political pretexts [30]; 3) HPAI H5N1 is not a disease with high priority in some regions, so awareness of the need and importance to report outbreaks is lacking [31]; and 4) Stakeholders do not like to report H5N1 outbreaks because of inadequate compensation for culled animals if reported [32]. It is difficult to measure the accurate underreporting situation becaue of lacking gold standard, but the mismatching case may reflect the underreporting situation to an extent.…”

“…stMRPP was calculated by the following formula [24]–[25],Where, the spatial position and outbreak date from OIE and FAO data were represented by ( s FAO i , t FAO i ), i  = 1, …, n FAO and ( s OIE i , t OIE i ), i  = 1, …, n OIE , respectively; d ( s FAOi , s FAOj ) and d ( s OIEi , s OIEj ) were used to represent the Euclidean distances between locations of s FAOi and s FAOj , and s OIEi and s OIEj , respectively. Significance test was based on Monte Carlo permutation procedures.…”

Review on the Application of Geographical Information Systems (GIS) in Veterinary Medicine

“…From a public health perspective, early warning of outbreaks with a known zoonotic potential of disease will enable control measures that can reduce human morbidity and Mortality rates. The main uses of early warning system include education as an aid to understanding the crucial elements involved in early detection and response to environmental threats [16].…”

Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems

“…Although animal disease surveillance has traditionally been implemented at national and sub-national levels, the increasing number of transboundary animal disease epidemics has highlighted the need for establishing such systems at broader scales. As a result, data warehouses and disease reporting systems such as World Animal Health Information Database (WAHID) (Jebara et al, 2012) and EMPRES Global Animal Disease Information System (EMPRES-i) (FAO: EMPRES transboundary animal disease bulletin, 2011 ;Farnsworth et al, 2010; were launched to encourage and facilitate data collection and sharing at a global level (Table 2). However, in addition to their original role, such data warehouses also provide researchers with cost-effective access to regularly updated spatial disease data, potentially leading to increased knowledge gains, without the need for costly and time-consuming primary research.…”