Geographical distribution of the health crisis of war in the Tigray region of Ethiopia.

War destroys health facilities and displaces health workers. It has a devastating impact on population health, especially in vulnerable populations. We assess the geographical distribution of the impact of war on healthcare delivery by comparing the pre-November 2020 and the November to June 2021 status of health facilities in the Tigray region of Ethiopia. Data were collected from February 2021 to June 2021, during an active civil war and an imposed communication blackout in Tigray. Primary data were collected and verified by multiple sources. Data include information on health facility type, geocoding and health facility status (fully functional (FF), partially functional (PF), not functional, no communication). Only 3.6% of all health facilities (n=1007), 13.5% of all hospitals and health centres (n=266), and none of the health posts (n=741), are functional. Destruction varies by geographic location; only 3.3% in Western, 3.3% in South Eastern, 6.5% in North Western, 8% in Central, 14.6% in Southern, 16% in Eastern and 78.6% in Mekelle are FF. Only 9.7% of health centres, 43.8% of general hospitals and 21.7% of primary hospitals are FF. None of the health facilities are operating at prewar level even when classified as FF or PF due to lack of power and water or essential devices looted or destroyed, while they still continue operating. The war in Tigray has clearly had a direct and devastating impact on healthcare delivery. Restoration of the destroyed health facilities needs to be a priority agenda of the international community.

Modelling temperature extremes in the Limpopo province: bivariate time-varying threshold excess approach.

A common problem that arises in extreme value theory when dealing with several variables (such as weather or meteorological) is to find an appropriate method to assess their joint or conditional multivariate extremal dependence behaviour. The method for choosing an appropriate threshold in peaks-over threshold approach is also another problem of endless debate. In this era of climate change and global warming, extreme temperatures accompanied by heat waves and cold waves pose serious economic and health challenges particularly in small economies or developing countries like South Africa. The present study attempts to address these problems, in particular, to deal with and capture dependencies in extreme values of two variables, by applying bivariate conditional extremes modelling with a time-varying threshold to Limpopo province's monthly maximum temperature series. Limpopo and North West provinces are the two hottest provinces in South Africa characterised by heat waves and the present study is carried out in the Limpopo province at Mara, Messina, Polokwane and Thabazimbi meteorological stations for the period 1994-2009. With the aim to model extremal dependence of maximum temperature at these four meteorological stations, two modelling approaches are applied: bivariate conditional extremes model and time-varying threshold. The latter approach was used to capture the climate change effects in the data. The main contribution of this paper is in combining these two approaches in bivariate extremal dependence modelling of maximum temperature extremes in the Limpopo province of South Africa. The findings of the study revealed both significant positive and negative extremal dependence in some pairs of meteorological stations. Among the major findings were the significant strong positive extremal dependence of Thabazimbi on high-temperature values at Mara and the strong negative extremal dependence of Polokwane on high-temperature values at Messina. The findings of this study play an important role in revealing information useful to meteorologists, climatologists, agriculturalists, and planners in the energy sector among others.

Why we need more coronavirus tests than we think we need.

James J. Cochran on the importance of testing a random sample.

Modelling non-stationary annual maximum flood heights in the lower Limpopo River basin of Mozambique.

In this article we fit a time-dependent generalised extreme value (GEV) distribution to annual maximum flood heights at three sites: Chokwe, Sicacate and Combomune in the lower Limpopo River basin of Mozambique. A GEV distribution is fitted to six annual maximum time series models at each site, namely: annual daily maximum (AM1), annual 2-day maximum (AM2), annual 5-day maximum (AM5), annual 7-day maximum (AM7), annual 10-day maximum (AM10) and annual 30-day maximum (AM30). Non-stationary time-dependent GEV models with a linear trend in location and scale parameters are considered in this study. The results show lack of sufficient evidence to indicate a linear trend in the location parameter at all three sites. On the other hand, the findings in this study reveal strong evidence of the existence of a linear trend in the scale parameter at Combomune and Sicacate, whilst the scale parameter had no significant linear trend at Chokwe. Further investigation in this study also reveals that the location parameter at Sicacate can be modelled by a nonlinear quadratic trend; however, the complexity of the overall model is not worthwhile in fit over a time-homogeneous model. This study shows the importance of extending the time-homogeneous GEV model to incorporate climate change factors such as trend in the lower Limpopo River basin, particularly in this era of global warming and a changing climate.