Communication of Epidemiological Data on Covid-19 In the State of Bahia, Brazil: An Experience Report of The State Epidemiological Surveillance Team

In view of the Covid-19 pandemic scenario, the dissemination of safe and correct information about the disease is essential to raise the population\u27s awareness about preventive measures and to direct public policies to deal with the state of emergency in public health. The objective of this paper is to describe the process of improvement and qualification of communication in the preparation of Covid-19 epidemiological data in the state of Bahia. The dissemination of epidemiological data contributed to the dissemination of information to the media in Bahia and Brazil. With the evolution of the disease, the need to present data in a more accessible way to the population has become urgent, requiring the team to institute measures to improve their work process in order to disseminate epidemiological data in a faster and more transparent manner. In contrast to this complex scenario, the permanent effort and dedication so that the data were consistent with the local reality, reliable and accessible, supporting the actions and strategies to fight the pandemic, can guarantee transparency to the population, the commitment of the information made available and the institution of social measures to control the pandemic

Modeling Disease Vector Occurrence When Detection Is Imperfect II: Drivers of Site-Occupancy by Synanthropic Triatoma brasiliensis in the Brazilian Northeast

Made available in DSpace on 2015-05-15T13:16:46Z (GMT). No. of bitstreams: 2 license.txt: 1914 bytes, checksum: 7d48279ffeed55da8dfe2f8e81f3b81f (MD5) marli_limaetal_IOC_2014.pdf: 698698 bytes, checksum: 0fabe5f5f7df31bef74aa8c2e3a204dd (MD5) Previous issue date: 2014Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Ecoepidemiologia de Doença da Chagas. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Ecoepidemiologia de Doença da Chagas. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Ecoepidemiologia de Doença da Chagas. Rio de Janeiro, RJ, Brasil.Secretaria Estadual de Saúde do Ceará. Fortaleza, CE, Brasil.Fiocruz Amazônia. Instituto Leônidas e Maria Deane. Manaus, AM, Brasil.Background: Understanding the drivers of habitat selection by insect disease vectors is instrumental to the design and operation of rational control-surveillance systems. One pervasive yet often overlooked drawback of vector studies is that detection failures result in some sites being misclassified as uninfested; naı¨ve infestation indices are therefore biased, and this can confound our view of vector habitat preferences. Here, we present an initial attempt at applying methods that explicitly account for imperfect detection to investigate the ecology of Chagas disease vectors in man-made environments. Methodology: We combined triplicate-sampling of individual ecotopes (n = 203) and site-occupancy models (SOMs) to test a suite of pre-specified hypotheses about habitat selection by Triatoma brasiliensis. SOM results were compared with those of standard generalized linear models (GLMs) that assume perfect detection even with single bug-searches. Principal Findings: Triatoma brasiliensis was strongly associated with key hosts (native rodents, goats/sheep and, to a lesser extent, fowl) in peridomestic environments; ecotope structure had, in comparison, small to negligible effects, although wooden ecotopes were slightly preferred. We found evidence of dwelling-level aggregation of infestation foci; when there was one such focus, same-dwelling ecotopes, whether houses or peridomestic structures, were more likely to become infested too. GLMs yielded negatively-biased covariate effect estimates and standard errors; both were, on average, about four times smaller than those derived from SOMs. Conclusions/Significance: Our results confirm substantial population-level ecological heterogeneity in T. brasiliensis. They also suggest that, at least in some sites, control of this species may benefit from peridomestic rodent control and changes in goat/sheep husbandry practices. Finally, our comparative analyses highlight the importance of accounting for the various sources of uncertainty inherent to vector studies, including imperfect detection. We anticipate that future research on infectious disease ecology will increasingly rely on approaches akin to those described here

All That Glisters Is Not Gold: Sampling-Process Uncertainty in Disease-Vector Surveys with False-Negative and False-Positive Detections

<div><p>Background</p><p>Vector-borne diseases are major public health concerns worldwide. For many of them, vector control is still key to primary prevention, with control actions planned and evaluated using vector occurrence records. Yet vectors can be difficult to detect, and vector occurrence indices will be biased whenever spurious detection/non-detection records arise during surveys. Here, we investigate the process of Chagas disease vector detection, assessing the performance of the surveillance method used in most control programs – active triatomine-bug searches by trained health agents.</p><p>Methodology/Principal Findings</p><p>Control agents conducted triplicate vector searches in 414 man-made ecotopes of two rural localities. Ecotope-specific ‘detection histories’ (vectors or their traces detected or not in each individual search) were analyzed using ordinary methods that disregard detection failures and multiple detection-state site-occupancy models that accommodate false-negative and false-positive detections. Mean (±SE) vector-search sensitivity was ∼0.283±0.057. Vector-detection odds increased as bug colonies grew denser, and were lower in houses than in most peridomestic structures, particularly woodpiles. False-positive detections (non-vector fecal streaks misidentified as signs of vector presence) occurred with probability ∼0.011±0.008. The model-averaged estimate of infestation (44.5±6.4%) was ∼2.4–3.9 times higher than naïve indices computed assuming perfect detection after single vector searches (11.4–18.8%); about 106–137 infestation foci went undetected during such standard searches.</p><p>Conclusions/Significance</p><p>We illustrate a relatively straightforward approach to addressing vector detection uncertainty under realistic field survey conditions. Standard vector searches had low sensitivity except in certain singular circumstances. Our findings suggest that many infestation foci may go undetected during routine surveys, especially when vector density is low. Undetected foci can cause control failures and induce bias in entomological indices; this may confound disease risk assessment and mislead program managers into flawed decision making. By helping correct bias in naïve indices, the approach we illustrate has potential to critically strengthen vector-borne disease control-surveillance systems.</p></div

Effects of covariates on the sensitivity of active Chagas disease vector searches in the lower Jaguaribe valley, Ceará, Brazil: model-averaged odds ratios (ORs) with approximate 85% confidence intervals (CIs) based on unconditional standard errors.

<p>The effect of a covariate is considered indistinguishable from zero when the CI crosses the dashed line at OR = 1.0 (black circles), positive if all values are >1.0 (red circles), and negative if all values are <1.0 (blue circles). Asterisks highlight covariates whose 95%CI overlaps 1.0. “SDEc” indexes, for each ecotope and vector-search round, whether detections occurred in other, same-dwelling ecotopes; see main text for further details. For each covariate effect (<i>β</i>_i), the OR is estimated as OR =  exp(<i>β</i>_i).</p

Model-averaged, adjusted slope coefficient estimates for detection covariates appearing in the subset of models with non-zero Akaike weights (see Table 4).

<p>Slope coefficient, model-averaged slope coefficient point estimate; SE, unconditional standard error; Lower and Upper, lower and upper limits of the approximate 85% confidence interval (CI). “SDEc”, detection in same-dwelling ecotopes. Coefficients highlighted in <b>bold</b> typeface have 85%CIs not overlapping zero; asterisks (*) indicate estimates whose 95%CI overlaps zero.</p><p>Model-averaged, adjusted slope coefficient estimates for detection covariates appearing in the subset of models with non-zero Akaike weights (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003187#pntd-0003187-t004" target="_blank">Table 4</a>).</p

Model-weighted average estimates of Chagas disease vector-search sensitivity (<i>p</i>₁₁) for different ecotope types.

<p>The means of model-averaged ecotope- and vector-search round-specific values are shown, with approximate 85% confidence intervals; in each panel, the mean vector-search sensitivity over all ecotope types (labeled “All”) is represented by an empty circle, and 50% sensitivity is highlighted by dashed lines. <b>A</b>, estimates from the complete dataset, with ecotopes ranked by mean vector-search sensitivity; the inset shows the relationship between model-predicted sensitivity and observed vector density; <b>B</b>, estimates for the lightly-infested locality of Russas; <b>C</b>, estimates for the heavily-infested locality of Jaguaruana. Ecotopes: BP, brick pile; Ho, house; HH, henhouse; PS, pigsty; SR, storeroom; GC, goat/sheep corral; CC, cattle corral; TP, tile pile; WP, woodpile. See <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003187#pntd.0003187.s003" target="_blank">Table S2</a> for further details.</p

Chagas disease vector ‘detection histories’ in 414 man-made ecotopes of the lower Jaguaribe valley in northeastern Brazil across three vector-search rounds: code, interpretation, and individual history frequencies.

<p>Results in the first three columns are coded as follows: 0 =  non-detection, 1 =  detection of only fecal streaks suggestive of triatomine bug presence, and 2 =  detection of at least one triatomine bug (any stage) or exuvia (molted ‘skin’) that could be identified without doubt.</p><p>Chagas disease vector ‘detection histories’ in 414 man-made ecotopes of the lower Jaguaribe valley in northeastern Brazil across three vector-search rounds: code, interpretation, and individual history frequencies.</p

Naïve indices of infestation (given as percentages) by Chagas disease vectors in 414 man-made ecotopes of the lower Jaguaribe valley in northeastern Brazil after three vector-search rounds.

<p><i>n</i>, number of ecotopes sampled within each class; ‘All detections’ include the detection of only fecal streaks identified (perhaps incorrectly) as those of triatomine bugs, whereas detections were considered ‘certain’ when at least one triatomine bug or exuvia (molted ‘skin’) were found and identified without doubt; S1 to S3, first to third vector-search rounds; Combined, combined results of all three vector-search rounds (percentage of ecotopes with at least one detection in at least one search round).</p><p>Naïve indices of infestation (given as percentages) by Chagas disease vectors in 414 man-made ecotopes of the lower Jaguaribe valley in northeastern Brazil after three vector-search rounds.</p

The subset of models with ΣAkaike weights ≈0.95, with models ranked by their QAICc scores.

<p>QAICc, quasi-AICc (AICc, Akaike information criterion corrected for sample size); ΔQAICc, difference in QAICc between each model and the lowest-QAICc (top-ranking) model; <i>w</i>_i, Akaike model weight; Likelihood, likelihood of each model, given the data (or relative strength of evidence for each model). SDEc, Same-Dwelling Ecotope infestation. See main text for the definitions and values of covariates, and ref. <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002861#pntd.0002861-Burnham1" target="_blank">[18]</a> for formulae and details on QAICc and related metrics.</p