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Introduction: Acute undifferentiated febrile illnesses (AUFIs) impose a large burden in the tropics. Understanding of AUFI's epidemiology is limited. Insufficient diagnostic capacity hinders the detection of outbreaks. The lack of interconnection in healthcare systems hinders timely response. We describe a protocol to study the epidemiology and aetiologies of AUFI and pathogen discovery in strategic areas of Latin America (LA)., Methods and Analysis: Global Infectious Diseases Network investigators comprising institutions in Colombia, Dominican Republic, México, Perú and the USA, developed a common cohort study protocol. The primary objective is to determine the aetiologies of AUFI at healthcare facilities in high-risk areas. Data collection and laboratory testing for viral, bacterial and parasitic agents are performed in rural and urban healthcare facilities and partner laboratories. Centralised laboratory and data management cores deploy diagnostic tests and data management tools. Subjects >6 years with fever for <8 days without localised infection are included in the cohort. They are evaluated during the acute and convalescent phases of illness. Study personnel collect clinical and epidemiological information. Blood, urine, nasal or pharyngeal swabs and saliva are collected in the acute phase and blood in convalescent phase. Specimens are banked at -80°C. Malaria, dengue and COVID-19 are tested onsite in the acute phase. The acute-phase serum is PCR tested for dengue, chikungunya, Venezuelan equine encephalitis, Mayaro, Oropouche, Zika, and yellow fever viruses. Paired convalescent and acute serum antibody titters are tested for arbovirus, Leptospira spp, and Rickettsia spp. Serum is used for viral cultures and next-generation sequencing for pathogen discovery. Analysis includes variable distributions, risk factors and regression models. Laboratory results are shared with health authorities and network members., Ethics and Dissemination: The protocol was approved by local ethics committees and health authorities. The results will be published in peer-reviewed journals. All study results are shared with local and regional health authorities., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: SARS-CoV-2 has caused a pandemic claiming more than 4 million lives worldwide. Overwhelming COVID-19 respiratory failure placed tremendous demands on healthcare systems increasing the death toll. Cost-effective prognostic tools to characterise the likelihood of patients with COVID-19 to progress to severe hypoxemic respiratory failure are still needed., Design: We conducted a retrospective cohort study to develop a model using demographic and clinical data collected in the first 12 hours of admission to explore associations with severe hypoxemic respiratory failure in unvaccinated and hospitalised patients with COVID-19., Setting: University-based healthcare system including six hospitals located in the Galveston, Brazoria and Harris counties of Texas., Participants: Adult patients diagnosed with COVID-19 and admitted to one of six hospitals between 19 March and 30 June 2020., Primary Outcome: The primary outcome was defined as reaching a WHO ordinal scale between 6 and 9 at any time during admission, which corresponded to severe hypoxemic respiratory failure requiring high-flow oxygen supplementation or mechanical ventilation., Results: We included 329 participants in the model cohort and 62 (18.8%) met the primary outcome. Our multivariable regression model found that lactate dehydrogenase (OR 2.36), Quick Sequential Organ Failure Assessment score (OR 2.26) and neutrophil to lymphocyte ratio (OR 1.15) were significant predictors of severe disease. The final model showed an area under the curve of 0.84. The sensitivity analysis and point of influence analysis did not reveal inconsistencies., Conclusions: Our study suggests that a combination of accessible demographic and clinical information collected on admission may predict the progression to severe COVID-19 among adult patients with mild and moderate disease. This model requires external validation prior to its use., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)