Associations of the COVID-19 pandemic with the reported incidence of important endemic infectious disease agents and syndromes in Pakistan

Abstract

Background Persons in Pakistan have suffered from various infectious diseases over the years, each impacted by various factors including climate change, seasonality, geopolitics, and resource availability. The COVID-19 pandemic is another complicating factor, with changes in the reported incidence of endemic infectious diseases and related syndromes under surveillance. Methods We assessed the monthly incidence of eight important infectious diseases/syndromes: acute upper respiratory infection (AURI), viral hepatitis, malaria, pneumonia, diarrhea, typhoid fever, measles, and neonatal tetanus (NNT), before and after the onset of the COVID-19 pandemic. Administrative health data of monthly reported cases of these diseases/syndromes from all five provinces/regions of Pakistan for a 3-year interval (March 2018–February 2021) were analyzed using an interrupted time series approach. Reported monthly incidence for each infectious disease agent or syndrome and COVID-19 were subjected to time series visualization. Spearman’s rank correlation coefficient between each infectious disease/syndrome and COVID-19 was calculated and median case numbers of each disease before and after the onset of the COVID-19 pandemic were compared using a Wilcoxon signed-rank test. Subsequently, a generalized linear negative binomial regression model was developed to determine the association between reported cases of each disease and COVID-19. Results In late February 2020, concurrent with the start of COVID-19, in all provinces, there were decreases in the reported incidence of the following diseases: AURI, pneumonia, hepatitis, diarrhea, typhoid, and measles. In contrast, the incidence of COVID was negatively associated with the reported incidence of NNT only in Punjab and Sindh, but not in Khyber Pakhtunkhwa (KPK), Balochistan, or Azad Jammu & Kashmir (AJK) & Gilgit Baltistan (GB). Similarly, COVID-19 was associated with a lowered incidence of malaria in Punjab, Sindh, and AJK & GB, but not in KPK and Balochistan. Conclusions COVID-19 was associated with a decreased reported incidence of most infectious diseases/syndromes studied in most provinces of Pakistan. However, exceptions included NNT in KPK, Balochistan and AJK & GB, and malaria in KPK and Balochistan. This general trend was attributed to a combination of resource diversion, misdiagnosis, misclassification, misinformation, and seasonal patterns of each disease.

Major changes in prevalence and genotypes of rotavirus diarrhea in Beijing, China after RV5 rotavirus vaccine introduction

“…isolation, close contact tracing, social distancing, mask-wearing, and hand hygiene, are also effective against enteric infectious diseases, including RVA diarrhea. 22 Reassortment and mutation have a major impact on the evolution of rotavirus and the emergence of new strains. 23 The complete genotypes of G8P [8] strain circulating in Beijing was "G8-P…”

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

“…Choropleth maps are widely used to visualize the state of a variable across a geographic area. In the papers, this technique was mostly used to represent the number of cases or cumulative cases in an area [17,22,24,27,46,48,52,56,58,[60][61][62][63]65,67,71,76,79,84,87,99,103,105,[113][114][115][116]119,121,127], along with the incidence of a disease [30,32,56,62,63,105,119,120,127]. It was also used to a lesser extent to visualize other measures, such as prevalence [35,64,76] and morbidity [111].…”

“…Of the 32 studies that used only conceptual representations of spatial information regarding an epidemic, the dominant visualization methods were line charts and networks [28,54,57,67,69,72,73,86,100,108,110,112,122,125,126]. Line charts were used principally to represent the number of cases and fatality rates.…”

“…According to our review, more than half of the 114 studies (67/114) used 2D or 3D line charts as depictions of time series to represent temporal data related to a pan-demic. They were not only used to depict the number of cases per week, month, or year [16,21,22,24,25,30,36,50,53,54,56,59,60,62,65,67,68,[71][72][73]75,79,[84][85][86][87]97,98,100,103,104,106,109,113,114,120,121,127] or cumulative cases [22,27,52,55,65,70,71,[98][99][100]105,…”

“…They were not only used to depict the number of cases per week, month, or year [16,21,22,24,25,30,36,50,53,54,56,59,60,62,65,67,68,[71][72][73]75,79,[84][85][86][87]97,98,100,103,104,106,109,113,114,120,121,127] or cumulative cases [22,27,52,55,65,70,71,[98][99][100]105,115] but were also used to depict other disease measures, such as the basic reproduction number…”