NOAA’s Oceanic Niño Index (ONI) is used to record for historical purposes the occurrence and duration of El Niño episodes, based on the monitoring of sea surface temperatures (SSTs) in the central Pacific Ocean. The ONI is used to identify the onset of an above average SST threshold that persists for several months, encompassing both the beginning and end of an El Niño episode. The first appearance of an anomalous seasonal value of 0.5 °C suggests with a high probability that an El Niño could emerge, but for heightened warnings, one must wait for several months. In this article, we proposed that the ONI value of 0.7 °C identifies a tipping point at which the El Niño event becomes locked in, which can provide additional lead time for mitigative actions to be taken by societal decision makers. Our preliminary findings suggest that a first appearance of 0.7 °C value could serve as a credible marker of El Niño’s locked-in phase, which can provide additional credibility to the current 0.5 °C El Niño onset indicator for at-risk societies to get ready for El Niño’s foreseeable societal and ecological impacts.

The aim of this rapid analysis was to investigate the spatial patterns of COVID-19 emergence across counties in Colorado. In the U.S. West, Colorado has the second highest number of cases and deaths, second only to California. Colorado is also reporting, like other states, that communities of color and low-income persons are disproportionately affected by COVID-19. Using GIS and correlation analysis, this study explored COVID-19 incidence and deaths from March 14 to April 8, 2020, with social determinants and chronic conditions. Preliminary results demonstrate that COVID-19 incidence intensified in mountain communities west of Denver and along the Urban Front Range, and evolved into new centers of risk in eastern Colorado. Overall, the greatest increase in COVID-19 incidence was in northern Colorado, i.e., Weld County, which reported the highest rates in the Urban Front Range. Social and health determinants associated with higher COVID-19-related deaths were population density and asthma, indicative of urban areas, and poverty and unemployment, suggestive of rural areas. Furthermore, a spatial overlap of high rates of chronic diseases with high rates of COVID-19 may suggest a broader syndemic health burden, where comorbidities intersect with inequality of social determinants of health.

This preliminary study examines the definition problem and challenges of climate forecasting and disaster responses associated with the El Niño costero (coastal) of 2017, which developed rapidly with no warning and had catastrophic effects in Peru. Such a localized El Niño was not documented since 1925. An initial review suggests that in addition to the characteristics of the event (surprise), government responses may have been inadequate (as media reported) because of conflicting forecast reports (U.S. and Peru), which provoked a hydrometeorological debate and stifled decision making. Partly to blame was the El Niño definition problem, which can cause uncertainty and affect perception of risk, depending on which region of the equatorial Pacific one uses to identify an event. Responses were further complicated by the fact that some regions within Peru were experiencing drought prior to the El Niño costero’s onset and impacts from the El Niño 2015–2016 were less than expected. Furthermore, a new government was in place, which may have hindered action. Thus, El Niño costero provides lessons to heed, not only with respect to the forecast information, but also with reference to the context of the forecast and disaster setting, which can influence disaster responses to hydrometeorological threats.

El Niño is a quasi-periodic pattern of climate variability and extremes often associated with hazards and disease. While El Niño links to individual diseases have been examined, less is known about the cluster of multi-disease risk referred to as an ecosyndemic, which emerges during extreme events. The objective of this study was to explore a mapping approach to represent the spatial distribution of ecosyndemics in Piura, Peru at the district-level during the first few months of 1998. Using geographic information systems and multivariate analysis, descriptive and analytical methodologies were employed to map disease overlap of 7 climate-sensitive diseases and construct an ecosyndemic index, which was then mapped and applied to another El Niño period as proof of concept. The main findings showed that many districts across Piura faced multi-disease risk over several weeks in the austral summer of 1998. The distribution of ecosyndemics were spatially clustered in western Piura among 11 districts. Furthermore, the ecosydemic index in 1998 when compared to 1983 showed a strong positive correlation, demonstrating the potential utility of the index. The study supports PAHO efforts to develop multi-disease based and interprogrammatic approaches to control and prevention, particularly for climate and poverty-related infections in Latin America and the Caribbean.