Epidemiology of invasive pneumococcal and Haemophilus influenzae diseases in Northwestern Ontario, Canada, 2010–2015

Eton, Vic; Schroeter, Annette; Kelly, Len; Kirlew, Michael; Tsang, Raymond S. W.; Ulanova, Marina

doi:10.1016/j.ijid.2017.09.016

Cited by 31 publications

(28 citation statements)

References 36 publications

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“…Disease prevalence of other encapsulated strains is 24 also increasing at an alarming rate and Hia disease burden now exceeds that of Hib during the pre- 25 Hib vaccination era in some regions and populations [15]. A recent report of particular interest 26 found that Hia constituted 50% of all H. influenzae cases between 2010 and 2015 in Northwestern 27 Ontario, Canada [16]. Importantly Hib also remains an issue in countries that have not implemented 28 a vaccination program [17].…”

Section: Introductionmentioning

confidence: 99%

hicap:in silicoserotyping of theHaemophilus influenzaecapsule locus

Watts

Holt

2019

Preprint

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Haemophilus influenzae exclusively colonises the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases including meningitis and sepsis. A key virulence determinant of H. influenzae is the polysaccharide capsule of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a to cap-f). H. influenzae type b (Hib) was historically responsible for the majority of invasive H. influenzae disease and prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, non-typeable H. influenzae emerged as the most dominant group causing disease but in recent years a resurgence of encapsulated H. influenzae strains has also been observed, most notably serotype a. Given the increasing incidence of encapsulated strains and the high frequency of Hib in countries without vaccination programs, there is growing interest in genomic epidemiology of H. influenzae. Here we present hicap, a software tool for rapid in silico serotype prediction from H. influenzae genome sequences. hicap is written using Python3 and is freely available at https://github.com/scwatts/hicap under the GPL3 license. To demonstrate the utility of hicap, we used it to investigate the cap locus diversity and distribution in 691 high-quality H. influenzae genomes from GenBank. These analyses identified cap loci in 95 genomes and confirmed the general association of each serotype with a unique clonal lineage and also identified occasional recombination between lineages giving rise to hybrid cap loci (2% of encapsulated strains). 7 non-typeable H. influenzae (NTHi) [2]. 8Biosynthesis of the polysaccharide capsule is controlled by the cap loci (cap-a to cap-f), which 9 each include three contiguous but functionally distinct regions (I, II, and III) ( Figure 1). Regions I 10 and III are common to all six cap loci, and are associated with cellular transport (bex operon, region 11

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Section: Introductionmentioning

confidence: 99%

hicap:in silicoserotyping of theHaemophilus influenzaecapsule locus

Watts

Holt

2019

Preprint

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“…The incidence rate of an invading disease adjusted to a population of 100,000 was suggested in the literature [6][7][8][9][10]. As of April 12, 2020, none were found incorporating CFR with both CFR100 and total con rmed cases(TCC) on a population of 100,000(here we name CFR100 and TCC100, respectively) in a study.…”

Section: Introductionmentioning

confidence: 89%

“…Numerous documents have reported using CFRs to compare death tolls of speci c diseases [5]. The incidence rates and CFRs adjusted by a population of 100,000 can be reasonably compared with one other [6][7][8][9][10]. We made an effort to combine both CFR(or CFT100) and TCC100 on a dashboard, which is novel, innovative, and rarely seen in past literature.…”

Section: What This Nding Adds To What We Already Knewmentioning

confidence: 99%

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Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: an observational study

Lin

Yeh

et al. 2020

Preprint

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Background: One of the key factors to understand the spread of COVID-19 is the case fatality rate(CFR) rates for counties/regions. However, many merely reported those CFRs on the basis of total confirmed cases(TCC) instead of per population of 100, 000. As such, the disparate definitions of CFR yield inconsistent results in COVID-19 situations. Whether both incident rate and the CFR can be indicators to identify a country/region hit by COVID-19 under(or out of) control is still unclear. This study aims to (1) develop a diagram to disperse both TCC and CFR on a population base of 100,000(namely TCC100 and CFR100, respectively) using the Kano model, (2)discuss selected countries/regions with success on preventative measures to keep COVID-19 under control(i.e., relatively lower TCC100 and CFR100 or CFR), and (3) design an app to display both TCC100 and CFR100 or CFR for all infected countries/regions. Methods: We downloaded the COVID-19 data of confirmed cases and deaths in countries/regions on a daily basis from the Github website. Three values, TCC100, CFR100, and CFR, were computed for each country/region and displayed on the Kano model. The lower TCC100 and CFR values were deemed as having the COVID-19 situation more under control. An app was developed to display both TCC100 and CFR100 on Google Maps. Results: The correlation coefficient(CC) between TCC100 and CFR100 is 0.92(t=37.87) based on 285 count data (i.e., 12, 225, and 48 dispersed on the Kano diagram) obtained in comparison to the pair of TCC100 and CFR with the CC=-0.02(t=-0.25) and counts(i.e., 56, 145, and 84 on the Kano diagram). The state of Washington, USA, was found having the highest TCC100(=3498.15), CFR100 (=16548.96) and CFR(=5%) in comparison to (1)Taiwan[TCC100(=1.65), CFR100 (=2.17), CFR(=1%)] and (2)South Korea[TCC100(=20.34), CFR100 (=39.8), CFR(=2%)] on April 12,2020. Conclusion: We illustrated both TCC100 and CFR along with CFR100 to compare countries/regions on a Kano diagram to understand the COVID-19 situations better. A larger TCC100 value was strongly associated with a larger CFR100 value that needs further verifications in the future. An app was developed to display both TCC100 and CFR for countries/regions on a dashboard laid over Google Maps.

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“…We identified 5,054 articles by searching the keywords "case fatality rate" on PubMed Central (PMC) [11]. The use of incidence per 100,000 population to describe a disease has been suggested in the literature [12][13][14][15][16]. Countries most struck by the COVID-19 based on the infectious density can be identified in this way.…”

Section: Comparisons Of Confirmed Cases Different On a Population Of mentioning

confidence: 99%

Comparison of COVID-19 Patterns Between Severely-Struck Italy and the Rest of the Worlds: An Observational Study

Jen

Yeh

Lin³

et al. 2020

Preprint

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Background The country of Italy was placed on lockdown when the number of cases of COVID-19 increased exponentially. It is of great concern which countries/regions would have the most similar patterns to Italy and the comparison of patterns, assuming homogeneity of variance within datasets, among regions worldwide, became necessary. There were no results in past literature regarding the identification of COVID-19 patterns among countries/regions. We were therefore motivated to propose an appropriate mathematic method, using a specific country/region (e.g., Italy), for example, in detecting similar patterns at the respective peak of the outbreak. A visual display highlighting COVID-19 patterns was proposed in this study using vector mathematics.Methods We downloaded COVID-19 outbreak numbers with confirmed cases in countries/regions on a daily basis from the Github website. The top peak point was identified for each country/region. Next, thirteen-time points were assigned before and after the inflection point. COVID-19 patterns were assessed by inspecting both similarity and distance using angle(or cosine theta in trigonometric function) and Chi-square statistics. Two sets of data on confirmed cases, one based on cases per 100,000 population and the other does not, were compared using rankings as well as four quadrants divided by similarity and distance. An app was developed to display regions with similar COVID-19 patterns to the selected country.Results The top four countries presenting with the most similar COVID-19 patterns to Italy were Switzerland, Norway, Iceland, and Luxembourg with Chi-square statistics per one freedom degree at 0.12, 0.37, 0.58, and 0.80, respectively, based on a population of 100,000. Visualizations with four quadrants and world map were shown on Google Maps. Rank correlation in rankings was − 0.03 between two sets of confirmed cases with and without using the basis of cases per population of 100,000.Conclusion We proposed and demonstrated a method using both criteria of similarity and distance from vectors in mathematics to identify countries that are most similar to Italy in terms of the pattern at the peak of the outbreak. The app was developed to display countries/regions with the most similar COVID-19 patterns to the targeted country/region using a dashboard laid on Google Maps. This method can be generalized and applied to study other countries/regions for the current pandemic and other past infectious disease in history.

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Epidemiology of invasive pneumococcal and Haemophilus influenzae diseases in Northwestern Ontario, Canada, 2010–2015

Abstract: In Northwestern Ontario, the incidence of invasive Hia disease exceeds that of H. influenzae type b (Hib) in the pre-Hib vaccine era. This provides strong support for the development of a new Hia vaccine. Improved pneumococcal vaccination of high-risk adults in the region is warranted.

Cited by 31 publications

References 36 publications

hicap:in silicoserotyping of theHaemophilus influenzaecapsule locus

hicap:in silicoserotyping of theHaemophilus influenzaecapsule locus

Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: an observational study

Comparison of COVID-19 Patterns Between Severely-Struck Italy and the Rest of the Worlds: An Observational Study

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