TY - JOUR
T1 - Spatio-temporal distribution of Crimean-Congo Hemorrhagic Fever and its relationship with climate factors in Pakistan
T2 - A decade-long experience from tertiary care laboratory network
AU - Abid, Muhammad Abbas
AU - Farooqi, Joveria
AU - Ghanchi, Najia
AU - Owais, Rabiya
AU - Sadiqa, Ayesha
AU - Shafaq, Humaira
AU - Khan, Erum
N1 - Publisher Copyright:
© 2025 Abid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2025/5
Y1 - 2025/5
N2 - Pakistan is at high-risk for Crimean-Congo Hemorrhagic Fever (CCHF) outbreaks due to its geographic location and disease burden in bordering countries. We aimed to study the spatio-temporal distribution of human CCHF cases in Pakistan and to observe its correlation with temperature, precipitation and seasonal variation. Retrospective data of test requests generated for CCHF at country-wide patient sample collection points (n = 307) across Pakistan from 2012 to 2023 was extracted for analysis. Average monthly temperature and precipitation data was used in the Poisson regression method to examine the effect on the number of cases. A total of 2,559 patients were clinically suspected of CCHF in 39 cities with 547 confirmed positive for CCHF in 10 cities using real-time PCR assay, with a positivity rate of 21.37% and a male predominance (84.6%). Most of the confirmed cases (57.6%, n = 315) were detected between 2016 and 2019 while 97.4% (n = 533) detected in 3 cities. Highest number of cases were reported during summer (p < 0.001) with 41.13% of confirmed cases reported in the months of August and September. A positive correlation of suspected cases with temperature was observed in Karachi and Quetta with a lag of zero months (p = 0.000), and a negative correlation was observed with precipitation for Karachi and Peshawar with a lag of 2 months (p = 0.000). Case fatality rate for CCHF patients admitted at Aga Khan University Hospital was 45.8%. CCHF is on the rise in Pakistan. Positive cases are concentrated within 3 cities where human and animal migration rates are high. Outbreak situations occur when multiple factors coincide. Seasonal and climatic patterns can be used as predictors of disease by policymakers for strict implementation on animal regulation, transport, and surveillance of animal migration to curtail outbreak situations in Pakistan.
AB - Pakistan is at high-risk for Crimean-Congo Hemorrhagic Fever (CCHF) outbreaks due to its geographic location and disease burden in bordering countries. We aimed to study the spatio-temporal distribution of human CCHF cases in Pakistan and to observe its correlation with temperature, precipitation and seasonal variation. Retrospective data of test requests generated for CCHF at country-wide patient sample collection points (n = 307) across Pakistan from 2012 to 2023 was extracted for analysis. Average monthly temperature and precipitation data was used in the Poisson regression method to examine the effect on the number of cases. A total of 2,559 patients were clinically suspected of CCHF in 39 cities with 547 confirmed positive for CCHF in 10 cities using real-time PCR assay, with a positivity rate of 21.37% and a male predominance (84.6%). Most of the confirmed cases (57.6%, n = 315) were detected between 2016 and 2019 while 97.4% (n = 533) detected in 3 cities. Highest number of cases were reported during summer (p < 0.001) with 41.13% of confirmed cases reported in the months of August and September. A positive correlation of suspected cases with temperature was observed in Karachi and Quetta with a lag of zero months (p = 0.000), and a negative correlation was observed with precipitation for Karachi and Peshawar with a lag of 2 months (p = 0.000). Case fatality rate for CCHF patients admitted at Aga Khan University Hospital was 45.8%. CCHF is on the rise in Pakistan. Positive cases are concentrated within 3 cities where human and animal migration rates are high. Outbreak situations occur when multiple factors coincide. Seasonal and climatic patterns can be used as predictors of disease by policymakers for strict implementation on animal regulation, transport, and surveillance of animal migration to curtail outbreak situations in Pakistan.
UR - https://www.scopus.com/pages/publications/105004906289
U2 - 10.1371/journal.pone.0320495
DO - 10.1371/journal.pone.0320495
M3 - Article
AN - SCOPUS:105004906289
SN - 1932-6203
VL - 20
JO - PLoS ONE
JF - PLoS ONE
IS - 5 MAY
M1 - e0320495
ER -
