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            "timestamp": "2022-04-09T01:55:08.600841+02:00",
            "action": 59,
            "target": "<h1>Referencias</h1>\n<h2>URL: </h2>\n<p><a href=\"http://www.who.int/malaria/publications/world-malaria-report-2017/en/\">http://www.who.int/malaria/publications/world-malaria-report-2017/en/</a>.</p>\n<h2>Referencias:</h2>\n<p>Organización Mundial de la Salud. Informe mundial sobre el paludismo 2017.</p>\n<p>.</p>\n<p>Organización Mundial de la Salud. Informe mundial sobre el paludismo 2008 [Internet]. Ginebra: Organización Mundial de la Salud; 2008. Disponible en: <a href=\"http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf\">http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf</a>.</p>\n<p>Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide Incidence of Malaria in 2009: Estimates, Time Trends, and a Critique of Methods. Mueller I, editor. PLoS Med. 2011 Dec 20;8(12):e1001142.</p>\n<p>R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Viena, Austria: R Foundation for Statistical Computing; 2016. Disponible en: <a href=\"http://www.R-project.org/\">http://www.R-project.org/</a>.</p>\n<p>Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. El efecto del control de la malaria en Plasmodium falciparum en África entre 2000 y 2015. Nature. 2015 Oct 8;526(7572):207&#x2013;11.</p>",
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            "timestamp": "2022-04-09T01:55:08.600781+02:00",
            "action": 30,
            "target": "<p><strong>URL:</strong> </p>\n<p>https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021</p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>1. World Health Organization. World Malaria Report 2021. </p>\n<p>2. World Health Organization. World Malaria Report 2008 [Internet]. Geneva: World Health Organization; 2008. Available from: http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf </p>\n<p>3. Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide Incidence of Malaria in 2009: Estimates, Time Trends, and a Critique of Methods. Mueller I, editor. PLoS Med. 2011 Dec 20;8(12):e1001142. </p>\n<p>4. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: http://www.R-project.org/ </p>\n<p> </p>\n<p>5. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015 Oct 8;526(7572):207&#x2013;11. </p>",
            "id": 23103527,
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            "timestamp": "2022-04-09T01:55:08.600712+02:00",
            "action": 59,
            "target": "<h2>Fuentes de discrepancias:</h2>\n<p>La incidencia estimada puede diferir de la incidencia comunicada por un Ministerio de Salud que puede verse afectada por:.</p>\n<ul>\n<li>la exhaustividad de la notificación: el número de casos notificados puede ser inferior a los casos estimados si el porcentaje de centros de salud que notifican en un mes es inferior al 100%</li>\n<li>el alcance de las pruebas de diagnóstico de la malaria (el número de portaobjetos examinados o las pruebas de diagnóstico rápido realizadas)</li>\n<li> el uso de instalaciones sanitarias privadas que no suelen estar incluidas en los sistemas de notificación.</li>\n</ul>",
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            "timestamp": "2022-04-09T01:55:08.600651+02:00",
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            "target": "<p><strong>Sources of discrepancies:</strong> </p>\n<p>The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by: </p>\n<ul>\n  <li>the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100% </li>\n  <li>the extent of malaria diagnostic testing (the number of slides examined or RDTs performed) </li>\n  <li>the use of private health facilities which are usually not included in reporting systems. </li>\n</ul>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.600586+02:00",
            "action": 59,
            "target": "<h1>Disponibilidad de datos</h1>\n<h2>Descripción:</h2>\n<p>109 países.</p>\n<h2>Series temporales:</h2>\n<p>Anualmente desde 2000.</p>\n<h2>Desagregación:</h2>\n<p>El indicador se estima a nivel de país.</p>",
            "id": 23103524,
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            "timestamp": "2022-04-09T01:55:08.600525+02:00",
            "action": 30,
            "target": "<p><strong>Data availability: </strong></p>\n<p>109 countries </p>\n<p> </p>\n<p><strong>Time series: </strong></p>\n<p>Annually since 2000 </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is estimated at country level. </p>",
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            "id": 23103522,
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            "timestamp": "2022-04-09T01:55:08.600391+02:00",
            "action": 59,
            "target": "<h2>Garantía de calidad:</h2>\n<ul>\n<li>Disponemos de un formulario estandarizado específico en función del estado del control de la malaria, la eliminación o la prevención de la reinfección. Llevamos a cabo una validación interna de los valores atípicos y de la integridad de los datos, y planteamos consultas a los países a través de las oficinas regionales para que los aclaren. Cuando es necesario, nos basamos en la información de evaluación de la calidad de los datos de fuentes externas, como los socios que trabajan en el seguimiento y la evaluación de la malaria.</li>\n<li>El Informe Mundial sobre el Paludismo se envía a los países a través de las oficinas regionales para su consulta y aprobación</li>.\n</ul>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.600330+02:00",
            "action": 30,
            "target": "<p>We collect data using a standardize form depending on the status of malaria control, elimination or prevention of reintroduction. We work closely with the collaborators centres and external reviewers to assure quality. </p>\n<p> </p>",
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            "timestamp": "2022-04-09T01:55:08.600264+02:00",
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            "id": 23103519,
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            "timestamp": "2022-04-09T01:55:08.600197+02:00",
            "action": 59,
            "target": "<h2>Agregados regionales:</h2>\n<p>El número de casos se agrega por regiones, y la incertidumbre se obtiene de la agregación de la distribución de cada país. La población de riesgo se agrega sin ningún otro ajuste. Las estimaciones a nivel global se obtienen a partir de la agregación de los valores de las regiones.</p>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.600136+02:00",
            "action": 30,
            "target": "<p>Number of cases are aggregated by region, and uncertainty obtained from the aggregation of each country&#x2019;s distribution. Population at risk is aggregated without any further adjustment. Estimation at global level is obtained from aggregation of the regional values.</p>",
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            "target": "<h2>Tratamiento de valores faltantes:</h2>\n<ul>\n  <li><strong><em>A nivel de país:</em></strong></li>\n</ul>\n<p>Para los valores faltantes de los parámetros (tasa de positividad de las pruebas y exhaustividad de la notificación) se utiliza una distribución basada en una mezcla de la distribución de los valores disponibles, si existe algún valor para el país o de la región en caso contrario. Los valores de los parámetros de comportamiento de búsqueda de la salud se imputan por interpolación lineal de los valores cuando se hicieron las encuestas o por extrapolación de la primera o última encuesta. Cuando no se dispone de datos, el número de casos se interpola teniendo en cuenta el crecimiento de la población.</p>\n<ul>\n  <li><strong><em>A nivel regional y mundial:</em></strong></li>\n</ul>\n<p>No aplicable.</p>",
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            "timestamp": "2022-04-09T01:55:08.600001+02:00",
            "action": 30,
            "target": "<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>For missing values of the parameters (test positivity rate and reporting completeness) a distribution based on a mixture of the distribution of the available values is used, if any value exists for the country or from the region otherwise. Values for health seeking behaviour parameters are imputed by linear interpolation of the values when the surveys where made or extrapolation of the first or last survey. When no reported data is available the number of cases is interpolated taking into account the population growth. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Not Applicable </p>\n<p> </p>",
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            "timestamp": "2022-04-09T01:55:08.599860+02:00",
            "action": 59,
            "target": "<h1>Metodología</h1>\n<h2>Método de cálculo:</h2>\n<p>La incidencia de la malaria (1) se expresa como el número de casos nuevos por cada 100.000 habitantes al año, con la población de un país derivada de las proyecciones realizadas por la División de Población de la ONU y la proporción total de riesgo estimada por el Programa Nacional de Control de la Malaria de un país. Más concretamente, el país estima cuál es la proporción de alto riesgo (H) y cuál es la proporción de bajo riesgo (L) y la población total de riesgo se estima como Población de la ONU x (H + L).</p>\n<p>El número total de casos nuevos, T, se estima a partir del número de casos de malaria notificados por un Ministerio de Salud, que se ajusta para tener en cuenta (i) el carácter incompleto de los sistemas de notificación (ii) los pacientes que buscan tratamiento en el sector privado, se automedican o no buscan tratamiento en absoluto, y (iii) el posible sobrediagnóstico por la falta de confirmación de los casos en el laboratorio. El procedimiento, que se describe en el <em>Informe Mundial sobre la Malaria 2009</em> (2), combina los datos notificados por los NMCP (por su sigla en inglés) (casos notificados, exhaustividad de la notificación y probabilidad de que los casos sean positivos para el parásito) con los datos obtenidos de las encuestas de hogares representativas a nivel nacional sobre el uso de los servicios sanitarios. Brevemente,</p>\n<p>T = (a + (c x e)/d) x (1 + h/g + ((1-g-h)/2/g)</p>\n<p>Donde:<br>a son los casos de malaria confirmados en el sector público<br>b son los casos sospechosos sometidos a prueba<br>c son los casos presuntos (no sometidos a prueba pero tratados como malaria)<br>d<em> </em>es la exhaustividad de la notificación<br>e<em> </em>es la tasa de positividad de la prueba (fracción positiva de malaria) = a/b<br>f<em> </em>es los casos en el sector público, calculada por (a + (c x e))/d<em><br></em>g<em> </em>es la fracción que busca tratamiento en el sector público<br>h es la fracción que busca tratamiento en el sector privado<br>i es la fracción que no busca tratamiento, calculada por (1-g-h)/2 <br>j son los casos en el sector privado, calculados por f x h/g<br>k son los casos que no están en el privado ni en el público, calculados por f x i/g<br>T es el total de casos, calculados por f + j + k.</p>\n<p>Para estimar la incertidumbre en torno al número de casos, se asumió que la tasa de positividad de las pruebas tiene una distribución normal centrada en el valor de la <em>Tasa de positividad de las pruebas</em> y una desviación estándar definida como.</p>\n<p>0,244 x Tasa de positividad de la prueba<sup>0,5547</sup></p>\n<p>y truncado para estar en el rango 0,1. Se supuso que la exhaustividad de la notificación tenía una de las tres distribuciones, dependiendo del rango o valor notificado por el NMCP. Si el rango era superior al 80%, se suponía que la distribución era triangular, con límites de 0,8 y 1 y el pico en 0,8. Si el rango era superior al 50%, se suponía que la distribución era rectangular, con límites de 0,5 y 0,8. Por último, si el rango era inferior al 50%, se suponía que la distribución era triangular, con límites de 0 y 0,5 y el pico en 0,5 (3) . Si la exhaustividad de la notificación se notificaba como un valor y era superior al 80%, se asumía una distribución beta con un valor medio del valor notificado (máximo del 95%) e intervalos de confianza (IC) del 5% alrededor del valor medio. Se supuso que las proporciones de niños para los que se buscó atención en el sector privado y en el sector público tenían una distribución beta, siendo el valor medio el valor estimado en la encuesta y la desviación estándar calculada a partir del rango de los intervalos de confianza (IC) estimados del 95% dividido por 4. Se supuso que la proporción de niños para los que no se buscó atención tenía una distribución rectangular, con el límite inferior 0 y el límite superior calculado como 1 menos la proporción que buscó atención en el sector público o privado.</p>\n<p>Los valores de la proporción que buscó atención se interpolaron linealmente entre los años que tienen una encuesta, y se extrapolaron para los años anteriores a la primera o posteriores a la última encuesta. Los valores perdidos para las distribuciones se imputaron utilizando una mezcla de la distribución del país, con igual probabilidad para los años en los que había valores o, si no había ningún valor para algún año del país, una mezcla de la distribución de la región para ese año. Los datos se analizaron con el programa estadístico R (4). Los intervalos de confianza se obtuvieron a partir de 10.000 extracciones de las distribuciones convulsivas. (Afganistán, Bangladesh, Bolivia (Estado Plurinacional de), Botsuana, Brasil, Camboya, Colombia, República Dominicana, Eritrea, Etiopía, Guayana Francesa, Gambia, Guatemala, Guyana, Haití, Honduras, India, Indonesia, República Democrática Popular Lao, Madagascar, Mauritania, Mayotte, Myanmar, Namibia, Nepal, Nicaragua, Pakistán, Panamá, Papúa Nueva Guinea, Perú, Filipinas, Ruanda, Senegal, Islas Salomón, Timor-Leste, Vanuatu, Venezuela (República Bolivariana de), Vietnam, Yemen y Zimbabue. En el caso de la India, los valores se obtuvieron a nivel sub-nacional utilizando la misma metodología, pero ajustando el sector privado para un factor adicional debido a la detección activa de casos, estimado como la relación de la tasa de positividad de las pruebas en la detección activa de casos sobre la tasa de positividad de las pruebas en la detección pasiva de casos. Se asumió que este factor tiene una distribución normal, con un valor medio y una desviación estándar calculados a partir de los valores notificados en 2010. Bangladesh, Bolivia, Botsuana, Brasil, Cabo Verde, Colombia, Guayana Francesa, Guatemala, Guyana, Haití, Honduras, Myanmar (desde 2013), República Dominicana, Ruanda, Surinam y Venezuela (República Bolivariana de) notifican los casos del sector privado y del público conjuntamente; por lo tanto, no se realizó ningún ajuste para el sector privado que busca tratamiento.</p>\n<p>Para algunos países africanos de alta transmisión, la calidad de la notificación de casos se considera insuficiente para aplicar las fórmulas anteriores. En estos casos, las estimaciones del número de casos de malaria se derivan de la información sobre la prevalencia del parásito obtenida de las encuestas a hogares. En primer lugar, los datos sobre la prevalencia del parásito procedentes de casi 600.000 registros de encuestas se ensamblaron dentro de un modelo geoestadístico bayesiano espacio-temporal, junto con covariables ambientales y socio-demográficas, y la distribución de datos sobre intervenciones como los mosquiteros tratados con insecticida, los fármacos antipalúdicos y el IRS. El modelo geoespacial permitió predecir la prevalencia de Plasmodium falciparum en niños de 2&#x2013;10 años, con una resolución de 5 &#xD7; 5 km2, en todos los países africanos donde la malaria es endémica para cada año entre 2000 y 2016 (véase <a href=\"http://www.map.ox.ac.uk/making-maps/\">http://www.map.ox.ac.uk/making-maps/</a> para conocer los métodos de elaboración de los mapas del Proyecto Atlas de la Malaria). En segundo lugar, se desarrolló un modelo de conjunto para predecir la incidencia de la malaria en función de la prevalencia del parásito. A continuación, se aplicó el modelo a la prevalencia estimada de parásitos para obtener estimaciones de la incidencia de casos de malaria a una resolución de 5 &#xD7; 5 km<sup>2</sup> para cada año desde 2000 hasta 2016. Los datos de cada área de 5 &#xD7; 5 km<sup>2</sup> se agregaron después dentro de los límites del país y de la región para obtener estimaciones nacionales y regionales de los casos de malaria (5). (Benín, Camerún, República Centroafricana, Chad, Congo, Costa de Marfil, República Democrática del Congo, Guinea Ecuatorial, Gabón, Guinea, Kenia, Malawi, Malí, Mozambique, Níger, Nigeria, Somalia, Sudán del Sur, Sudán, Togo y Zambia)</p>\n<p>Para la mayoría de los países de eliminación, el número de casos autóctonos registrados por los NMCP se comunica sin más ajustes (Arabia Saudí, Argelia, Argentina, Belice, Bután, Cabo Verde, China, Comoras, Costa Rica, República Popular Democrática de Corea, Yibuti, Ecuador, El Salvador, Irán (República Islámica de), Iraq, Malasia, México, Paraguay, República de Corea, Santo Tomé y Príncipe, Sudáfrica, Surinam, Suazilandia y Tailandia).</p>",
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            "target": "<p>Malaria incidence (1) is expressed as the number of new cases per 100,000 population per year with the population of a country derived from projections made by the UN Population Division and the total proportion at risk estimated by a country&#x2019;s National Malaria Control Programme. More specifically, the country estimates what is the total proportion of the population at risk of malaria and then, for each year, the total population at risk is estimated as the UN Population for that year, times the proportion of the population at risk at baseline. The same proportion of the population at risk is used for the entire time series to ensure comparability of estimates through time. </p>\n<p>The total number of new cases, T, is estimated from the number of malaria cases reported by a Ministry of Health which is adjusted to take into account (i) incompleteness in reporting systems (ii) patients seeking treatment in the private sector, self-medicating or not seeking treatment at all, and (iii) potential over-diagnosis through the lack of laboratory confirmation of cases. The procedure, which is described in the <em>World malaria report 2009 </em>(2), combines data reported by NMCPs (reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health-service use. Briefly,</p>\n<p>T=(a+(c &#xD7; e)/d)&#xD7;(1+h/g+((1&#x2212;g&#x2212;h)/2)/g)</p>\n<p>where:<br>a is the number of malaria cases confirmed in public sector<br>b is the number of suspected cases tested<br>c is the number of presumed cases (not tested but treated as malaria)<br><em>d </em>is the reporting completeness<br><em>e </em>is the test positivity rate (malaria positive fraction) = a/b<br><em>f </em>is the estimated cases in public sector, calculated by (a + (c x <em>e</em>))/<em>d</em><br><em>g </em>is the fraction seeking treatment in public sector<br><em>h </em>is the fraction seeking treatment in private sector<br><em>i </em>is the fraction not seeking treatment, calculated by (1-<em>g</em>-<em>h</em>)/2<br><em>j </em>is the cases in private sector, calculated as <em>f x h/g</em><br><em>k </em>is the cases not in private and not in public, calculated by <em>f </em>x <em>i</em>/<em>g</em><br><em>T </em>is total cases, calculated by <em>f </em>+ <em>j </em>+ <em>k</em> </p>\n<p>To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the <em>Test positivity rate</em> value and standard deviation defined as</p>\n<p>0.244 &#xD7; Test positivity rate<sup>0.5547</sup></p>\n<p>and truncated to be in the range 0, 1. Reporting completeness was assumed to have one of three distributions, depending on the range or value reported by the NMCP. If the value was reported as a range greater than 80%, the distribution was assumed to be triangular, with limits of 0.8 and 1.0, and the peak at 0.8. If the value was more than 50% but less than or equal to 80%, the distribution was assumed to be rectangular, with limits of 0.5 and 0.8. Finally, if the value was less than or equal to 50%, the distribution was assumed to be triangular, with limits of 0 and 0.5, and the peak at 0.5 <em>(3)</em>. If the reporting completeness was reported as a value and was more than 80%, a beta distribution was assumed, with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% around the mean value. The fraction of children brought for care in the public sector and in the private sector was assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation being calculated from the range of the estimated 95% CIs. The fraction of children not brought for care was assumed to have a rectangular distribution, with the lower limit being 0 and the upper limit calculated as 1 minus the proportion that were brought for care in the public and private sectors. The three distributions (fraction seeking treatment in public sector, fraction seeking treatment in private sector only and fraction not seeking treatment) were constrained to add up to 1.</p>\n<p>Sector-specific care-seeking fractions were linearly interpolated between the years that had a survey and were extrapolated for the years before the first or after the last survey. The parameters used to propagate uncertainty around these fractions were also imputed in a similar way or, if there was no value for any year in the country or area, were imputed as a mixture of the distributions of the region for that year. CIs were obtained from 10 000 draws of the convoluted distributions. The data were analysed using the R statistical software (4). This method was used was used for countries and areas outside the WHO African Region, and for low-transmission countries and areas in the African Region: Afghanistan, Bangladesh, Bolivia (Plurinational State of), Botswana, Brazil, Cambodia, Colombia, the Dominican Republic, Eritrea, Ethiopia, French Guiana, the Gambia, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, the Lao People&#x2019;s Democratic Republic, Madagascar, Mauritania, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, the Philippines, Rwanda, Senegal, Solomon Islands, Timor-Leste, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen and Zimbabwe. Bangladesh, Bolivia (plurinational State of), Botswana, Brazil, Colombia, Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Rwanda, and Venezuela (Bolivarian Republic of) report cases from the private and public sector together; therefore, no adjustment for private sector seeking treatment was made while for Indonesia, 25% of the private was assumed to be reported in the public sector since 2017. For India, the values were obtained at subnational level using the same methodology, but adjusting the private sector for an additional factor due to the active case detection, estimated as the ratio of the test positivity rate in the active case detection over the test positivity rate for the passive case detection. This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010. An additional adjustment was applied in several states in India, to control for the reductions in reported testing rates associated with disruptions in health services related to the COVID-19 pandemic. </p>\n<p>For some high-transmission African countries the quality of case reporting is considered insufficient for the above formulae to be applied. In such cases estimates of the number of malaria cases are derived from information on parasite prevalence obtained from household surveys. First, data on parasite prevalence from nearly 60 000 survey records were assembled within a spatiotemporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data distribution on interventions such as ITNs, antimalarial drugs and IRS. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2&#x2013;10 years, at a resolution of 5 &#xD7; 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2016 (see <a href=\"https://malariaatlas.org/\">https://malariaatlas.org/</a> for methods on the development of maps by the Malaria Atlas Project). Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence. The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 &#xD7; 5 km2 resolution for each year from 2000 to 2020. Data for each 5 &#xD7; 5 km2 area were then aggregated within country and regional boundaries to obtain both national and regional estimates of malaria cases (5). In 2020, additional cases estimated using this method were added, to account for the disruptions in malaria prevention, diagnostic and treatment services as a result of the COVID-19 pandemic and other events that occurred during this year. Disruption information was reported per country and was obtained from the national pulse surveys on continuity of essential health services during the COVID-19 pandemic conducted by WHO (first round in May&#x2013;July 2020 and second in January&#x2013;March 2021). This method was applied in the following countries: Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Congo, C&#xF4;te d&#x2019;Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mozambique, the Niger, Nigeria, Sierra Leone, Somalia, South Sudan, the Sudan, Togo, Uganda, the United Republic of Tanzania and Zambia</p>\n<p>For most of the elimination or near elimination countries, the number of indigenous cases registered by the NMCPs are reported without further adjustments. (Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Cabo Verde, China, Comoros, Costa Rica, Democratic People&#x2019;s Republic of Korea, Djibouti, Ecuador, Egypt, El Salvador, Eswatini, Georgia, Iran (Islamic Republic of), Iraq, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Oman, Paraguay, Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, Syrian Arab Republic, Thailand, Turkey, Turkmenistan, United Arab Emirates and Uzbekistan). </p>",
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            "target": "<h2>Comentarios y limitaciones:</h2>\n<p>La incidencia estimada puede diferir de la incidencia comunicada por un Ministerio de Salud que puede verse afectada por:.</p>\n<ul>\n<li>la exhaustividad de la notificación: el número de casos notificados puede ser inferior a los casos estimados si el porcentaje de centros de salud que notifican en un mes es inferior al 100%</li>\n<li>el alcance de las pruebas de diagnóstico de la malaria (el número de portaobjetos examinados o las pruebas de diagnóstico rápido realizadas)</li>\n<li> el uso de instalaciones sanitarias privadas que no suelen estar incluidas en los sistemas de notificación.</li>\n<li> el indicador se estima sólo donde se produce la transmisión de la malaria</li>\n</ul>",
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            "timestamp": "2022-04-09T01:55:08.599667+02:00",
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            "target": "<p>The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by:</p>\n<ul>\n  <li>the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100%</li>\n  <li>the extent of malaria diagnostic testing (the number of slides examined or RDTs performed)</li>\n  <li>the use of private health facilities which are usually not included in reporting systems.</li>\n  <li>the indicator is estimated only where malaria transmission occurs.</li>\n</ul>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599598+02:00",
            "action": 59,
            "target": "<h2><strong>Justificación</strong>:</h2>\n<p>Medir las tendencias de la morbilidad de la malaria e identificar los lugares donde el riesgo de enfermedad es mayor. Con esta información, los programas pueden responder a tendencias inusuales, como las epidemias, y dirigir los recursos a las poblaciones más necesitadas. Estos datos también sirven para informar sobre la asignación global de recursos para la malaria, como, por ejemplo, a la hora de definir los criterios de elegibilidad para la financiación del Fondo Mundial.</p>",
            "id": 23103510,
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599537+02:00",
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            "target": "<p>To measure trends in malaria morbidity and to identify locations where the risk of disease is highest. With this information, programmes can respond to unusual trends, such as epidemics, and direct resources to the populations most in need. These data also serves to inform global resource allocation for malaria such as when defining eligibility criteria for Global Fund finance.</p>",
            "id": 23103509,
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            "timestamp": "2022-04-09T01:55:08.599468+02:00",
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            "target": "",
            "id": 23103508,
            "action_name": "String updated in the repository",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599400+02:00",
            "action": 59,
            "target": "<h1>Compiladores de datos</h1>\n<p>La Unidad de Vigilancia, Seguimiento y Evaluación del Programa Mundial de Control del Paludismo es la encargada de recopilar y procesar toda la información pertinente. Las estimaciones nacionales de algunos países se realizan en colaboración con la Universidad de Oxford (Proyecto Atlas del Paludismo).</p>",
            "id": 23103507,
            "action_name": "String updated in the repository",
            "url": "https://hosted.weblate.org/api/changes/23103507/?format=api"
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        {
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599339+02:00",
            "action": 30,
            "target": "<p>The Surveillance, Monitoring and Evaluation Unit of the Global Malaria Control Programme is the responsible to compile and process all the relevant information. National estimates for some countries are estimated in collaboration with the Malaria Atlas Project which has been designated a WHO collaborating centre in geospatial disease modelling.</p>",
            "id": 23103506,
            "action_name": "Source string changed",
            "url": "https://hosted.weblate.org/api/changes/23103506/?format=api"
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599272+02:00",
            "action": 59,
            "target": "<h1>Proveedores de datos</h1>\n<p>El Programa Nacional de Control de la Malaria es el responsable de recopilar la información en cada país.</p>",
            "id": 23103505,
            "action_name": "String updated in the repository",
            "url": "https://hosted.weblate.org/api/changes/23103505/?format=api"
        },
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599183+02:00",
            "action": 30,
            "target": "<p>The National Malaria Control Program is the responsible to collect the information at each country.</p>",
            "id": 23103504,
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599119+02:00",
            "action": 59,
            "target": "<h2>Publicación de datos:</h2>\n<p>Los datos se publican anualmente. La próxima publicación está prevista para diciembre de 2018.</p>",
            "id": 23103503,
            "action_name": "String updated in the repository",
            "url": "https://hosted.weblate.org/api/changes/23103503/?format=api"
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        {
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.599058+02:00",
            "action": 30,
            "target": "<p>Data is released yearly.</p>",
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            "timestamp": "2022-04-09T01:55:08.599003+02:00",
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            "target": "<h1>Calendario</h1>\n<h2>Recopilación de datos:</h2>\n<p>Los datos se recopilan cada año.</p>",
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            "timestamp": "2022-04-09T01:55:08.598950+02:00",
            "action": 30,
            "target": "<p>Data is collected every year.</p>",
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            "timestamp": "2022-04-09T01:55:08.598898+02:00",
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            "target": "<h2>Proceso de recolección:</h2>\n<p>La contraparte oficial de cada país es el Programa Nacional de Control de la Malaria del Ministerio de Salud.</p>",
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            "action_name": "String updated in the repository",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598849+02:00",
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            "target": "<p>The official counterpart for each country is the National Malaria Control Program at the Ministry of Health.</p>",
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            "target": "<h1>Conceptos y definiciones</h1>\n<p><strong>Definición:</strong>.</p>\n<p>La incidencia del paludismo se define como el número de nuevos casos de paludismo por cada 1.000 personas de riesgo cada año.</p>\n<p><strong>Conceptos:</strong>.</p>\n<p>El caso de paludismo se define como la aparición de una infección de paludismo en una persona cuya presencia de parásitos de paludismo en la sangre se ha confirmado mediante una prueba de diagnóstico. La población considerada es la población de riesgo de la enfermedad.</p>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598746+02:00",
            "action": 30,
            "target": "<p><strong>Definition:</strong> </p>\n<p>Incidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk each year. </p>\n<p><strong>Concepts:</strong></p>\n<p>A case of malaria is defined as the occurrence of malaria infection in a person in whom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. The population considered is the population at risk of the disease.</p>\n<p> </p>",
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598688+02:00",
            "action": 59,
            "target": "<h1>Información institucional</h1>\n<h2>Organización: </h2>\n<p>Programa Mundial sobre Malaria de la Organización Mundial de la Salud (OMS).</p>",
            "id": 23103495,
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598635+02:00",
            "action": 30,
            "target": "<p>Global Malaria Programme at World Health Organization(WHO)</p>",
            "id": 23103494,
            "action_name": "Source string changed",
            "url": "https://hosted.weblate.org/api/changes/23103494/?format=api"
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598594+02:00",
            "action": 59,
            "target": "<p>Febrero de 2021 </p>",
            "id": 23103493,
            "action_name": "String updated in the repository",
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            "target": "2022-03-31",
            "id": 23103492,
            "action_name": "Source string changed",
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            "id": 23103491,
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            "user": null,
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            "timestamp": "2022-04-09T01:55:08.598460+02:00",
            "action": 59,
            "target": "<p>Indicador 3.3.3: Incidencia de la malaria por cada 1.000 habitantes</p>",
            "id": 23103490,
            "action_name": "String updated in the repository",
            "url": "https://hosted.weblate.org/api/changes/23103490/?format=api"
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            "user": null,
            "author": null,
            "timestamp": "2022-04-09T01:55:08.598373+02:00",
            "action": 30,
            "target": "<p>Indicator 3.3.3: Malaria incidence per 1,000 population</p>",
            "id": 23103489,
            "action_name": "Source string changed",
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            "timestamp": "2022-02-10T22:10:25.722036+01:00",
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            "id": 17112751,
            "action_name": "Resource updated",
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        {
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            "timestamp": "2022-02-10T22:10:25.721996+01:00",
            "action": 59,
            "target": "<h1>Referencias</h1>\n<h2>URL: </h2>\n<p><a href=\"http://www.who.int/malaria/publications/world-malaria-report-2017/en/\">http://www.who.int/malaria/publications/world-malaria-report-2017/en/</a>.</p>\n<h2>Referencias:</h2>\n<p>Organización Mundial de la Salud. Informe mundial sobre el paludismo 2017.</p>\n<p>.</p>\n<p>Organización Mundial de la Salud. Informe mundial sobre el paludismo 2008 [Internet]. Ginebra: Organización Mundial de la Salud; 2008. Disponible en: <a href=\"http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf\">http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf</a>.</p>\n<p>Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide Incidence of Malaria in 2009: Estimates, Time Trends, and a Critique of Methods. Mueller I, editor. PLoS Med. 2011 Dec 20;8(12):e1001142.</p>\n<p>R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Viena, Austria: R Foundation for Statistical Computing; 2016. Disponible en: <a href=\"http://www.R-project.org/\">http://www.R-project.org/</a>.</p>\n<p>Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. El efecto del control de la malaria en Plasmodium falciparum en África entre 2000 y 2015. Nature. 2015 Oct 8;526(7572):207&#x2013;11.</p>",
            "id": 17112750,
            "action_name": "String updated in the repository",
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        },
        {
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            "component": "https://hosted.weblate.org/api/components/sdg-metadata/3-3-3/?format=api",
            "translation": "https://hosted.weblate.org/api/translations/sdg-metadata/3-3-3/es/?format=api",
            "user": null,
            "author": null,
            "timestamp": "2022-02-10T22:10:25.721962+01:00",
            "action": 30,
            "target": "<p><strong>URL:</strong></p>\n<p><a href=\"https://www.who.int/publications/i/item/9789240015791\">https://www.who.int/publications/i/item/9789240015791</a></p>\n<p><strong>References:</strong></p>\n<p>1. World Health Organization. World Malaria Report 2020 2020. </p>\n<p>2. World Health Organization. World Malaria Report 2008 [Internet]. Geneva: World Health Organization; 2008. Available from: http://apps.who.int/iris/bitstream/10665/43939/1/9789241563697_eng.pdf </p>\n<p>3. Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide Incidence of Malaria in 2009: Estimates, Time Trends, and a Critique of Methods. Mueller I, editor. PLoS Med. 2011 Dec 20;8(12):e1001142.</p>\n<p>4. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: http://www.R-project.org/ </p>\n<p>5. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015 Oct 8;526(7572):207&#x2013;11.</p>",
            "id": 17112749,
            "action_name": "Source string changed",
            "url": "https://hosted.weblate.org/api/changes/17112749/?format=api"
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        {
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            "user": null,
            "author": null,
            "timestamp": "2022-02-10T22:10:25.721913+01:00",
            "action": 59,
            "target": "<h2>Fuentes de discrepancias:</h2>\n<p>La incidencia estimada puede diferir de la incidencia comunicada por un Ministerio de Salud que puede verse afectada por:.</p>\n<ul>\n<li>la exhaustividad de la notificación: el número de casos notificados puede ser inferior a los casos estimados si el porcentaje de centros de salud que notifican en un mes es inferior al 100%</li>\n<li>el alcance de las pruebas de diagnóstico de la malaria (el número de portaobjetos examinados o las pruebas de diagnóstico rápido realizadas)</li>\n<li> el uso de instalaciones sanitarias privadas que no suelen estar incluidas en los sistemas de notificación.</li>\n</ul>",
            "id": 17112748,
            "action_name": "String updated in the repository",
            "url": "https://hosted.weblate.org/api/changes/17112748/?format=api"
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            "user": null,
            "author": null,
            "timestamp": "2022-02-10T22:10:25.721858+01:00",
            "action": 30,
            "target": "<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by: </p>\n<ul>\n  <li>the completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100% </li>\n  <li>the extent of malaria diagnostic testing (the number of slides examined or RDTs performed) </li>\n  <li>the use of private health facilities which are usually not included in reporting systems. </li>\n</ul>",
            "id": 17112747,
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            "user": null,
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            "timestamp": "2022-02-10T22:10:25.721823+01:00",
            "action": 59,
            "target": "<h1>Disponibilidad de datos</h1>\n<h2>Descripción:</h2>\n<p>109 países.</p>\n<h2>Series temporales:</h2>\n<p>Anualmente desde 2000.</p>\n<h2>Desagregación:</h2>\n<p>El indicador se estima a nivel de país.</p>",
            "id": 17112746,
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            "user": null,
            "author": null,
            "timestamp": "2022-02-10T22:10:25.721791+01:00",
            "action": 30,
            "target": "<p><strong>Data availability: </strong></p>\n<p>109 countries</p>\n<p><strong>Time series:</strong></p>\n<p>Annually from 2000</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is estimated at country level.</p>",
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            "user": null,
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            "timestamp": "2022-02-10T22:10:25.721755+01:00",
            "action": 59,
            "target": "<h2>Garantía de calidad:</h2>\n<ul>\n<li>Disponemos de un formulario estandarizado específico en función del estado del control de la malaria, la eliminación o la prevención de la reinfección. Llevamos a cabo una validación interna de los valores atípicos y de la integridad de los datos, y planteamos consultas a los países a través de las oficinas regionales para que los aclaren. Cuando es necesario, nos basamos en la información de evaluación de la calidad de los datos de fuentes externas, como los socios que trabajan en el seguimiento y la evaluación de la malaria.</li>\n<li>El Informe Mundial sobre el Paludismo se envía a los países a través de las oficinas regionales para su consulta y aprobación</li>.\n</ul>",
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            "user": null,
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            "timestamp": "2022-02-10T22:10:25.721720+01:00",
            "action": 59,
            "target": "<h2>Métodos y directrices a disposición de los países para la recopilación de los datos a nivel nacional:</h2>\n<p>La información es proporcionada por el NMCP (por su sigla en inglés) de cada país a través de una aplicación DHIS 2 creada específicamente para ello.</p>",
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