Evidence map›Paper›PMID 30636103›Full record

ArticleTransboundary and emerging diseases2019

Seasonal patterns and space-time clustering of porcine reproductive and respiratory syndrome (PRRS) cases from 2008 to 2016 in Vietnam.

Hu Suk Lee, Thanh Long Pham, Tien Ngoc Nguyen, Mihye Lee, Barbara Wieland

Abstract read
In one paragraph

Article in Transboundary and emerging diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hu Suk LeeInternational Livestock Research Institute (ILRI), Hanoi, Vietnam.ORCID http://orcid.org/0000-0002-8731-9836
Thanh Long PhamEpidemiology Division, Department of Animal Health, Hanoi, Vietnam.
Tien Ngoc NguyenEpidemiology Division, Department of Animal Health, Hanoi, Vietnam.
Mihye LeeMedical Microbiology Department, The Royal Bournemouth Hospital, Bournemouth, UK.
Barbara WielandInternational Livestock Research Institute (ILRI), Addis Ababa, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Porcine reproductive and respiratory syndrome (PRRS) is an important disease in pig production and is endemic in Vietnam. No nationwide studies have been carried out to understand the spread of PRRS in Vietnam. The main objective of this study was to identify the seasonal patterns and space-time clusters of PRRS from 2008 to 2016 using national surveillance data in Vietnam. A total of 614,219 cases were reported during the period. There was a seasonal pattern with single peak by region (except North Central Coast, showing double peaks in March and June). The seasonal plots from the Northern regions showed a higher peak between March and April, whereas the four regions from Southern part displayed a higher peak between June and August. Overall, outbreaks from the northern part of Vietnam tended to occur 3-4 months earlier than the southern part. When the spatial window was set at 50%, space-time cluster analysis found that the first cluster occurred in the Red River Delta (RRD) (radius: 82.17 km; ratios: 5.5; period: Mar-May/2010) and the second (radius: 50.8 km; ratios: 10.61; period: Aug-Oct/2011) in the Mekong River Delta (MRD) region. Four other clusters were observed in the central and Southern parts. Our findings might provide better insight into the distribution of clusters and temporal patterns of PRRS in Vietnam. This study may provide policy makers with valuable information on the hotspot areas and timing of outbreaks. Also, it identifies when and where national control program could be implemented more efficiently by targeting resources for the prevention and control of PRRS.

Indexed as

AnimalsDisease OutbreaksEpidemiological MonitoringIncidencePorcine Reproductive and Respiratory SyndromePorcine respiratory and reproductive syndrome virusSeasonsSpace-Time ClusteringSwineVietnamtransmissionveterinary epidemiologyvirus

Identifiers

PMID30636103
PMCPMC6850339

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.