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opencc-by-4.0Mar 2024View details →

Data and code for: The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)

Data and R code for  The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)

opencc-by-4.0Apr 2024View details →

BTyperDB: a community-curated, global atlas of Bacillus cereus sensu lato genomes for epidemiological surveillance

The ability to cause foodborne illness, anthrax, and other infections has been attributed to numerous lineages within Bacillus cereus sensu lato (s.l.). However, existing pathogen surveillance databases facilitate dangerous pathogen misidentifications when applied to B. cereus s.l., potentially hindering outbreak or bioterrorism attack response efforts. To address this, we developed BTyperDB (<a href="http://www.btyper.app/">www.btyper.app</a>), an atlas of B. cereus s.l. genomes with standardized, community-curated metadata. BTyperDB aggregates all publicly available B. cereus s.l. genomes (including >2,600 previously unassembled genomes) with novel genomes donated by laboratories around the world, nearly doubling the number of publicly available B. cereus s.l. genomes. To showcase its utility for pathogen surveillance, we use BTyperDB to identify emerging anthrax toxin- and capsule-harboring lineages. Overall, our study provides insight into the epidemiology of an under-studied group of emerging pathogens and highlights the benefits of inclusive, community-driven metadata FAIRification efforts.

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Online Appendix and Cetacean Datasets for: The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, i.e. lacking morphological and molecular data, which we call occurrences, provide invaluable information to reconstruct the past number of lineages. Here, we present new methodological developments around the Fossilized Birth-Death Process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death and sampling rates; and (iii) reconstruct the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical datasets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the COVID-19 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics.

opencc-zeroDec 2021View details →

opencc-by-4.0Nov 2021View details →

An epidemiological Study to Assess Household Transmission & Associated Risk Factors for COVID-19 Disease amongst Residents of Delhi, India.

Executive summary: Studying the spread and epidemiological characteristics of COVID-19 virus specially in household settings are needed to prepare our self-better in preventing and controlling this epidemic. In this study we proposed a conceptual framework of four level of determinates and tried to understand the transmission dynamics of COVID-19 among household contacts along with clinical, epidemiological and virologic characteristics of the infection.  Aims & Objectives: <ol> <li>the proportion of asymptomatic cases and symptomatic cases;</li> <li>the incubation period of COVID-19 and the duration of infectiousness and of detectable shedding;</li> <li>the serial interval of COVID-19 infection; </li> <li>clinical risk factors for COVID-19, and the clinical course and severity of disease; </li> <li>high-risk population subgroups;</li> <li>the secondary infection rate and secondary clinical attack rate of COVID-19 infection among household contacts; and</li> <li>the associations of various factors across four dimensions interaction associated with risk of transmission</li> </ol> Methodology: This was a case-ascertained study where all susceptible contacts of a laboratory confirmed COVID-19 case were studied prospective for four weeks after their enrolment. It was done in New Delhi, during the end of first wave as well as whole second wave from December 2020 to July 2021. The study team collected the key information by questionnaire along with blood and oro-nasal swab during the household visits. Follow-up was done on day 7, 14 and 28 for observing the disease characteristic and symptomatology along with confirmation by serum and oro-nasal swab testing. Daily characteristics of the infection were noted by the participants on symptoms diary. Results: We enrolled 99 households, each having one laboratory-confirmed COVID-19 index case along with their 318 susceptible contacts. By the end of the follow-up, secondary infection rate was seen at 55.5%, while seroconversion in 46.6%.  Hospitalization and case fatality rate was 3.83% and 1.7% respectively. Among epidemiological characteristics we observed serial interval of 8.0 ± 6.7 days, generation time 3.8  ± 6.4, while secondary attack rate was 54.9%. The predictors of secondary infection among individual contact level were being female (OR:2.13, 95% CI:1.27 - 3.57), age of the household contact (1.01;1.00 - 1.03), symptoms at baseline (3.39; 1.61- 7.12) and during follow-up (3.18; 1.64 - 6.19), while only symptoms during follow-up (3.81: 1.43 - 10.14) and being RT-PCR positive (8.32; 3.22 -21.54) was significantly and independently associated with seroconversion among household contacts. Among index case-level age of the primary case (1.03; 1.01 -1.04) and any symptoms during follow-up (6.29; 1.83-21.63) significantly and independently associated with secondary infection while any symptoms during follow-up was associated with seroconversion among household contacts. Among household-level characteristics having more rooms (4.44; 2.16 - 9.13) independently associated with secondary infection, while more rooms (3.98; 1.23 -12.90) along with overcrowding (0.37; 0.16 - 0.82) associated with seroconversion. Among contact pattern only taking care of the index case (2.02;1.21- 3.38) was significantly and independently associated with secondary infection, while none was associated with seroconversion. Conclusion: A high secondary cases and secondary attack rate was seen in our study. This highlights the need to adopts strict measure and advocate COVID appropriate behaviours in order to break the transmission chain at household level. The targeted approach at household contacts with higher risk would be efficient in limiting the development of infection among susceptible contacts.

Fig. 5 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 5. Healthy skin and fibropapilloma in Pristimantis megalops (A). Cross section of the healthy skin of Pristimantis megalops (B): The epidermis is thin, can be seen pigmented layer and layers of collagen in the dermis. Cross section of Fibropapilloma (C). Detail of fibroblasts forming fibropapilloma (D): the tissue is highly vascularized. Detail of the epidermis and collagen layers covering fibropapilloma (E–F). Histochemical staining in Fibropapilloma (F): The layers of collagen in the dermis can be seen in magenta color. fb: fibroblasts; col: layers of collagen; de: dermis; ep: epidermis; gla: dermal glands; hs: healthy skin; lp: pigmented layer; sm: striated muscle tissue; tu: tumor or fibropapilloma; vt: vascular tissue. Photographs by Edgar Javier Rincón Barón.

Fig. 1 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 1. Map of the Serrania de San Lorenzo, Sierra Nevada de Santa Marta, Colombia. Red square area highlights the Estación Experimental de San Lorenzo to 2,200 meters.

Fig. 3 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 3. Healthy individuals of Atelopus laetissimus (A) (Bufonidae); Atelopus nahumae (B) (Bufonidae); Pristimantis megalops (C) (Craugastoridae); and Ikakogi tayrona (D) (Centrolenidae). Photographs by Luis Alberto Rueda Solano.

Fig. 4 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 4. Sick individuals of Ikakogi tayrona (A, B same individual); Pristimantis megalops (C, D), and malformation in Atelopus nahumae (E) and Atelopus laetissimus (F) found in epidemiological surveillance 2008–2014 in La Estación Experimental de San Lorenzo (2,200 meters), Sierra Nevada de Santa Marta, North of Colombia. Photographs by Luis Alberto Rueda Solano (A, B, D), Cesar Molina (C); Andres Rocha Usuga (E, F).

Fig. 6 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 6. Monitoring of Atelopus laetissimus through the years in the Quebrada San Lorenzo Serrania de San Lorenzo, SNSM. Circle = average number obtained from individuals in each year (n = 2 samples for the years 2008, 2013 and 2014) (n = 1 sample for 2009); Error Bars = maximum and minimum individual in each year. Dotted line = trend in the number of individuals over time.

Fig. 2 in Epidemiological surveillance and amphibian assemblage status at the Estación Experimental de San Lorenzo, Sierra Nevada de Santa Marta, Colombia

Fig. 2. Serrania de San Lorenzo (B) Querbrada San Lorenzo (A) Sierra Nevada de Santa Marta, Colombia. Photographs by Luis Alberto Rueda Solano.

opencc-by-4.0Dec 2022View details →

Figure 2 in Epidemiological aspects of scorpionic accidents in a municipality in Brazil's northeastern

Figure 2. Analysis of scorpionic accidents between the years 2008 to 2018 in the neighborhoods of Arapiraca-AL.

opencc-by-4.0Dec 2022View details →

Figure 1 in Epidemiological aspects of scorpionic accidents in a municipality in Brazil's northeastern

Figure 1. Percentage of households with inadequate sanitation in neighborhoods in the urban area of Arapiraca-AL, from 2008 to 2018.

Fig. 3 in Epidemiology and molecular phylogeny of Babesia sp. in Little Penguins Eudyptula minor in Australia

Fig. 3. Agarose gel electrophoresis of amplification products obtained through nested PCR tests targeting the 18S rRNA gene of Babesia (primers Bab5.1/BabB followed by RLBF/RLBR) or the mitochondrial cytochrome b gene of Haemoproteus/Plasmodium (primers HaemNFI/HaemNR3 followed by HaemF/HaemR2). The following samples are represented: (a) captive-born little penguin chick, negative blood smear; (b) adult wild little penguin, negative blood smear; (c) Babesia-infected adult wild little penguin, as confirmed through blood smear; (d) Haemoproteus-infected adult tropical screech owl, as confirmed through blood smear; (e) Plasmodium-inoculated chicken, raised in arthropod-free environment; (f) blood parasite-free chicken, raised in arthropodfree environment.

Fig. 1 in Epidemiology and molecular phylogeny of Babesia sp. in Little Penguins Eudyptula minor in Australia

Fig. 1. Geographic distribution of sampling locations, southeast Australia. Site details are given in Table 1. The geographic distribution of little penguins (black area) is shown in the top right map (adapted from Marchant and Higgins, 1990).

Fig. 4 in Epidemiology and molecular phylogeny of Babesia sp. in Little Penguins Eudyptula minor in Australia

Fig. 4. Maximum likelihood phylogenetic tree of the 18S rRNA gene of the studied Babesia lineages. Lineages identified in this study are emphasized in red, and other avianinfecting lineages are emphasized in blue. For each lineage, the following information is provided: morphospecies (Genbank ascension number) host species. For avianinfecting lineages, the geographic location is also provided. Branch lengths are drawn proportionally to evolutionary distance (scale bar is shown). For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.

Fig. 2 in Epidemiology and molecular phylogeny of Babesia sp. in Little Penguins Eudyptula minor in Australia

Fig. 2. Babesia sp. in the blood smear of a little penguin. Individual details: TAS- 124, male, adult, moulting, sampled at "Darlington Foreshore" (Maria Island, Tasmania) in 21/02/2013, Genbank ascension number KP144323, Giemsa stain.

Fig. 4 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 4. Predicted mean and standard error body condition scores show associations with infection presence and season, with co-infected buffalo in much lower condition in the early wet season (Table S2). Coccidia infection status is represented with C– and C+; nematode infection status is represented with N– and N+.

Fig. 5 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 5. Season and co-infection differences in nematode aggregation. (a) Aggregation patterns in calves (b) and non-calves. (c) In non-calves, the distribution of nematode parasites in the late wet season shows that k is not significantly different in coccidia positive vs. negative buffalo. (d) In the early wet season coccidia positive buffalo have a truncated distribution, resulting in significantly reduced aggregation. Arrows indicate nematode intensity values in the tail of the distribution of coccidia negative buffalo. Coccidia infection status is represented with C– and C+.

Fig. 3 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 3. Patterns of parasite egg/oocyst counts with co-infection for (a) nematodes and (b) coccidia. (c), the mean nematode intensity in calves is higher in early wet season than in the late wet season independent of co-infection with coccidia. (d) Co-infection with coccidia alters the seasonal patterns of nematode intensity in non-calf buffalo (>1 year, juvenile through senescent). Calf vs. non-calf division is based on model paramters (Table 1).

Fig. 2 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Fig. 2. Age specific patterns of parasite prevalence with co-infection. (a) Prevalence of nematodes is higher in buffalo co-infected with coccidia (C+) compared to coccidia negative buffalo (C–) in all age categories (N = 33, 318, 166, 272, 162 for calf, juvenile, subadult, adult and senescent C– buffalo; N = 58, 237, 55, 54, 20 for C+ buffalo). (b) Prevalence of coccidia is higher in buffalo co-infected with nematodes (N+) compared to nematode negative buffalo (N–) in calf, juvenile, subadult, and senescent buffalo but not adult buffalo (N = 13, 107, 92, 144, 56 for calf, juvenile, subadult, adult and senescent N– buffalo; N = 38, 448, 129, 208, 100 for N+ buffalo).