The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

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Melanotic neuroectodermal tumour of infancy is an extremely rare neoplasm arising in newborns and young children, typically involving the face or cranium. A case arising from the maxilla, requiring extensive resection with a near-total maxillectomy, is presented. A thorough review of the literature on this unusual tumour is provided, with emphasis on prognostic factors and appropriate treatment.

Background: Scrub typhus, an important cause of undifferentiated fever, is grossly neglected and often misdiagnosed in low and middle income countries like Nepal. The main aim of this study was to describe the clinico-laboratory profile, drug used in treatment, predictor of PICU admission and therapeutic outcome of serologically confirmed scrub typhus among Nepalese children.Methods: A prospective observational study was carried out in children aged up to 14 years with serologically (IgM ELISA) diagnosed Scrub typhus, admitted in a tertiary care hospital of central Nepal between Jan 2019 to Dec 2019.Results: All 100 children with scrub typhus presented with fever. Other symptoms and sign were cough (29%), abdominal distension (22%) hepatomegaly (45%), splenomegaly (28%), crepitation (10%) and eschar (6%). Similarly, thrombocytopenia (72%), and increased liver enzymes SGPT (51%) and SGOT (62%) were found. Co-infection with dengue (5%) brucella (5%) and UTI (5%) were seen. Thirty six percent has some form of complication. Fifty eight percent of children were treated with azithromycin and 25% treated with doxycycline. The mean length of hospital stay was 6.68 ±2.97 days with a mean duration of defervescence being 30.07 ± 26.65 hours. The increased risk of PICU admission was found in those children with crepitation in chest (OR: 15.17, 95% CI: 3.4-66.8) during presentation and those children not getting azithromycin as treatment (OR: 3.8, 95% CI: 1.2-11.7)Conclusions: Scrub typhus should be considered as a differential diagnosis in any community acquired acute undifferentiated febrile illness regardless of the presence of an eschar. Sepsis, meningitis and pneumonia are important complications. Child having crepitation on presentation has an increased chance admission in critical care unit. The child receiving azithromycin has less chance to land in PICU.Keywords: Clinico-laboratory profile; complications; fever; scrub typhus.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.