Research article

Negative effects of high public debt on health systems facing pandemic crisis: Lessons from COVID-19 in Europe to prepare for future emergencies

  • The investigation goal here was to analyze how the level of public debt affects preparedness of health systems to face emergencies. In particular, this study examined the negative effects of high public debt on health systems of European countries in the presence of the COVID-19 pandemic crisis. Empirical evidence revealed that European countries with a lower level of government debt as a percentage of GDP both in 2009 and 2019 (the period before the arrival of the pandemic) had lower COVID-19 fatality rates compared to countries with higher levels of public debt. The explanation is that high levels of public debt in countries trigger budget constraints that limit their ability to allocate resources to healthcare systems (e.g., health expenditures and investments), weakening health system performance and causing systemic vulnerability and lower preparedness during emergencies, such as with the COVID-19 pandemic. Implications of health policies are suggested to improve strategies of crisis management.

    Citation: Mario Coccia, Igor Benati. Negative effects of high public debt on health systems facing pandemic crisis: Lessons from COVID-19 in Europe to prepare for future emergencies[J]. AIMS Public Health, 2024, 11(2): 477-498. doi: 10.3934/publichealth.2024024

    Related Papers:

    [1] Dorra Djebbi-Simmons, Mohammed Alhejaili, Marlene Janes, Joan King, Wenqing Xu . Survival and inactivation of human norovirus GII.4 Sydney on commonly touched airplane cabin surfaces. AIMS Public Health, 2020, 7(3): 574-586. doi: 10.3934/publichealth.2020046
    [2] Terri Rebmann, Kristin D. Wilson, Travis Loux, Ayesha Z. Iqbal, Eleanor B. Peters, Olivia Peavler . Outcomes, Approaches, and Challenges to Developing and Passing a Countywide Mandatory Vaccination Policy: St. Louis County’s Experience with Hepatitis A Vaccine for Food Service Personnel. AIMS Public Health, 2016, 3(1): 116-130. doi: 10.3934/publichealth.2016.1.116
    [3] Christy Pu . The Influence of the Common Cold on Self-Rated Health: A Population-Based Study. AIMS Public Health, 2015, 2(3): 247-255. doi: 10.3934/publichealth.2015.3.247
    [4] Yenisetti SC, Muralidhara . Beneficial Role of Coffee and Caffeine in Neurodegenerative Diseases: A Minireview. AIMS Public Health, 2016, 3(2): 407-422. doi: 10.3934/publichealth.2016.2.407
    [5] S. J. Olshansky, L. Hayflick . The Role of the WI-38 Cell Strain in Saving Lives and Reducing Morbidity. AIMS Public Health, 2017, 4(2): 127-138. doi: 10.3934/publichealth.2017.2.127
    [6] Junji Takaya . Small for Gestational Age and Magnesium: Intrauterine magnesium deficiency may induce metabolic syndrome in later life. AIMS Public Health, 2015, 2(4): 793-803. doi: 10.3934/publichealth.2015.4.793
    [7] Viorica Munteanu, Antonella Petaccia, Nicolae Contecaru, Emanuele Amodio, Carlo Virginio Agostoni . Paediatric acute rheumatic fever in developed countries: Neglected or negligible disease? Results from an observational study in Lombardy (Italy). AIMS Public Health, 2018, 5(2): 135-143. doi: 10.3934/publichealth.2018.2.135
    [8] Sudip Bhattacharya, Amarjeet Singh . Phasing out of the Universal Mega Dose of Vitamin-A Prophylaxis to Avoid Toxicity. AIMS Public Health, 2017, 4(1): 38-46. doi: 10.3934/publichealth.2017.1.38
    [9] Ali Hussain Ansari, Saqib Hussain Ansari, Mubarak Jabeen Salman, Muhammad Usman Hussain Ansari, Rawshan Jabeen . A scoping review on the obstacles faced by beta thalassemia major patients in Pakistan- Matter of policy investment. AIMS Public Health, 2024, 11(4): 1105-1124. doi: 10.3934/publichealth.2024057
    [10] Katherine Karacaoglu, Calum F Leask . Staff views of a hospital at home model implemented in a Scottish care setting. AIMS Public Health, 2021, 8(3): 467-478. doi: 10.3934/publichealth.2021036
  • The investigation goal here was to analyze how the level of public debt affects preparedness of health systems to face emergencies. In particular, this study examined the negative effects of high public debt on health systems of European countries in the presence of the COVID-19 pandemic crisis. Empirical evidence revealed that European countries with a lower level of government debt as a percentage of GDP both in 2009 and 2019 (the period before the arrival of the pandemic) had lower COVID-19 fatality rates compared to countries with higher levels of public debt. The explanation is that high levels of public debt in countries trigger budget constraints that limit their ability to allocate resources to healthcare systems (e.g., health expenditures and investments), weakening health system performance and causing systemic vulnerability and lower preparedness during emergencies, such as with the COVID-19 pandemic. Implications of health policies are suggested to improve strategies of crisis management.



    As the requirements facing batteries increase—both in terms of demand and complexity—the ability to understand, control, and predict battery performance becomes ever more urgent. Given the importance of the thermal effect on batteries’ behaviors with respect to a wide range of battery properties, including performance, life span, and—critically—safety, it would seem axiomatic that characterization of battery thermal profiles (particularly with respect to thermal monitoring during cycling) is a crucial part of battery development [1,2,3,4]. Indeed, relevant models, which range from simple one-dimensional thermal models to highly complex three-dimensional coupled thermal and electrochemical analyses, show that irrespective of device, non-uniform temperature distributions can be present during operation; often resulting in issues for the devices in question [5,6,7,8,9,10,11,12]. Moreover, due to the critical importance of safety, there has been considerable interest in developing and improving the thermal handling of battery systems—particularly with respect to that undertaken by the battery management system (BMS) [13,14,15,16].

    This highlights the relevance of temperature with respect to operational and safety analyses for fuel cells and batteries, which is subsequently reflected in the varied and abundant literature produced regarding monitoring the thermal characteristics and behaviors of these devices [17,18,19,20]. Consequently, techniques appropriate for the elucidation of a greater understanding of these phenomena are in great demand. It should be no surprise, therefore, that thermal monitoring has become a popular area of research [21]. One increasingly popular analytical tool is infrared thermography (IT), as it is a safe, non-destructive, and contactless technology, which may be used to determine the thermal profile of a battery [20,21,22]. However, in addition to providing insights into the thermal profile of a battery (i.e. thermal monitoring) it must be noted that IT may also identify hotspots in situ (while the battery is still in operation) [21,23]. In many conventional battery systems, hotspots represent an indication of a fault and so, in this way, IT represents a powerful tool—capable not only of thermal monitoring but also the hitherto mostly neglected possibility [21,24] of early fault detection.

    In general, nondestructive evaluation (NDE) by infrared thermography may be divided into two methods: 1) passive infrared thermography (PIT) and 2) active infrared thermography (AIT) [25]. In essence, PIT involves detecting abnormal temperature fields—the presence of which may indicate anomalies. This technique, mainly used for qualitative inspection and localization purposes, is relatively simple to implement and has been used to good effect for diagnosis in other fields [26]. By contrast, AIT consists of analyzing the transient heat flux of the thermal scene after its solicitation by an external thermal source, and has proven to be a powerful tool when PIT is too sensitive to variations of surrounding conditions, requires too long a duration, or does not provide sufficient thermal contrast [27]. While AIT does offer advantages over PIT, the simplicity of PIT makes it an ideal starting point for evaluation. Indeed, previous work has demonstrated that PIT in particular may be applied to lead acid batteries in order to successfully detect observable failures [24]. However, it also highlighted the difficulties in attempting to correlate voltage and thermal signal. This may be attributed to the complexities of the internal chemistries and design of the battery system making such attempts at direct correlation extremely challenging.

    In order to address this issue, we believe that a refined approach may prove more viable. This may best be summarized as first correlating the battery’s thermal behavior—and formation of hotspots in particular—to the internal reactions, before using this understanding to develop the key descriptors for electrochemical performance prognosis with respect to early fault detection.

    In this way, by linking the internal battery reaction to its thermal and electrical behavior, we suggest it will be possible to a) develop a non-destructive early fault monitoring technique; b) develop a refined thermal monitoring tool to ensure smooth and safe battery operation; and c) provide a framework of improved understanding to facilitate future iterative improvements.

    However, in order to justify future development of this technique, it is first necessary to demonstrate the added value of such an approach. Thus, in order to validate our proposed technique as a proof-of-concept, we use—for the first time—a combination of in operando thermal imaging, electrochemical measurement, and post-mortem analysis to determine the utility and viability of the proposed technique.

    In this work, we describe in detail our experimental methodology which consists of:

    1) Gathering a dataset of thermal/electrochemical behaviors of pre-selected lead acid batteries.

    2) Using PIT to identify key internal features of interest.

    3) Undertaking post-mortem analyses to characterize these identified areas.

    4) Correlating the batteries’ most relevant internal reactions to thermal behavior, so as to validate the utility of this approach.

    5) Examining these proof-of-concept results with respect to applicability of this method to early fault detection in batteries.

    Finally, we will summarize the results and discuss in detail the implications with regard to the on-going development of a fault detection model.

    Two types of batteries were selected for analysis in this work: systems which are at the beginning-of-life (henceforth ‘BoL systems’), having undergone formation cycles only, and systems which have undergone field aging resulting from use in real-world telecommunications powerplant (henceforth ‘degraded systems’).

    These batteries consist of a series of 6 cells, each of which has a grid of alternately 4 anode and 3 cathode plates, each insulated by a glass fiber separator impregnated with electrolyte (see schematic in Figure 1).

    Figure 1.  Schematic of a) battery; b) cutaway of cell; c) internal grid structure.

    The two selected batteries were placed in a climate chamber, controlled by an Omron E5CC temperature controller, with padded walls to limit environmental reflections (Figure 2). The electrochemical tests were carried out using an Arbin LBT22043 cycler. The cycler’s ADC (analog to digital converter) has 24 bit resolution, and was configured to acquire data every minute.

    Figure 2.  Picture of the two batteries in the test chamber.

    Both batteries were left at room temperature, in the chamber, until thermal equilibrium with the environment was achieved.

    After this period of thermal stabilization, the following protocol was simultaneously applied to both batteries:

    ● Discharge at 0.1C (−0.45 A) until the voltage reached 10.8 V

    ● A subsequent 2 h rest step

    ● Charge at 0.3C (1.35 A) under constant current until the voltage reached 14.7 V

    ● Charge at 14.7 V (constant voltage) until current is 0, 01C (0,045 A)

    ● Subsequently, a floating mode at 13.6 V for 7.5 hours

    During the cycling procedure, the thermal behaviors of the two batteries were simultaneously measured using a FLIR A6752SC infrared camera (3–5 µm, 50 mm, f/2.5 lens). This infrared camera was controlled using the FLIR ResearchIR Max software (Version 4.40.9.30). To ensure thermal data was collected at times in accordance with the electrochemical data, thermal images were acquired every minute.

    After selection of the appropriate area for measurement (as denoted in Figure 3b), the total thermal scene was represented by images of size 320 × 256 pixels (Figure 3a). The experimental thermograms were calculated by averaging, at each time step, of two zones of the thermal scene (131 × 91 pixels per battery, see Figure 3b).

    Figure 3.  a) Thermal scene of the two studied batteries (t = 0 s); b) two zones, in black, representing the average experimental thermograms calculus. The batteries on the left-side and right-side of each picture corresponds to the BoL and the degraded system respectively.

    The recorded data was post-treated using the FLIR ResearchIR Max software and the scientific programming language GNU Octave [28].

    Post-mortem analysis was then carried out on relevant samples extracted from the battery. Crystallization was characterized using Scanning Electron Microscope (SEM) FEI Quanta 250. The crystal structures of the lead species present were then identified by X-ray diffraction (XRD) using a Bruker D8 Discover diffractometer (monochromatic Cu Kα radiation, λ = 1.54053 Å), at room temperature, within the 2θ range of 15 −80°.

    From the body of experimental data, representative results were selected for characterization. Figure 4 shows the thermal scene at different times during the experiment (total duration c.a. 25 hours).

    Figure 4.  (From left to right and from top to bottom) Thermal scene at time t = 0 s, 10000 s, 20000 s, 30000 s, 40000 s, 50000 s, 60000 s and 70000 s. The batteries on the left-side and right-side of each picture corresponds to the BoL and the degraded system.

    As expected, the use of passive infrared thermography allowed to detect the damaged battery by means of its excessive energy release (Figure 4, right side). One can notice that the results (images) are given in the arbitrary units of the infrared camera, proportional to the received radiative flux (an excessive energy release appears in yellow in the previous figure). Then, experimental thermograms were calculated for each battery (see 2.2. Experimental setup and protocol). These thermograms were obtained after conversion into apparent temperature using the internal calibration of the infrared camera and by arbitrarily fixing the emissivity of the thermal scene at 0.92. They were also corrected to take into account the variation in temperature of the environment (± 0.7 ℃).

    The thermograms and the corresponding currents are plotted as a function of time in Figure 5.

    Figure 5.  Current and infrared profiles for a) BoL and b) degraded systems.

    At the end of the experiment (t > 70000 s), an infrared image was taken at each side of the degraded battery. By applying a simple thresholding treatment, the localization of the defective area is easily observed (see Figure 6).

    Figure 6.  Center, estimated location of defect; surrounding, infrared images of the four faces of the degraded battery.

    It should also be noted that the quantitative study of the most emitting zones can provide a powerful tool to refine this location. Indeed, the amplitude of its thermal imprint on a given surface of a battery, as well as its diffusive spread, made it possible to estimate the probable location of a defect. Thus, in the case of the damaged battery studied in this paper, a weaker thermal diffusion observed on the ‘left and lower’ (Figure 6) faces indicate the proximity of the energy source. In the same way, the higher amplitude of the signal on the ‘left’ face indicated that the heat source is located near this face. Finally, by exploiting the data of the four faces, the probable location of the defect has been proposed (Figure 6, center). In order to validate the relevance of this approach a post-mortem study was conducted on the defective battery.

    In order to ensure relevant post-mortem information was obtained for the aged battery, the points of interest were identified using detailed thermal mapping (see Figure 7). As can be seen, the area is located in upper part of block A, between cell 2 and 3.

    Figure 7.  a) Thermal imaging of the battery’s edge with cell identification; b) Thermal imaging of the battery’s front with block identification; c) schematic of the battery’s design with hotpot and the block and cell assignation.

    In order to physically access this location, the battery was opened in a well-ventilated fume cupboard using Dremel cutting tools in accordance with safety protocols to ensure good laboratory practice.

    Visual inspection (see Figure 8) revealed the anode plate was covered in molten plastic, the area of which corresponded with the estimated defect location. The 3D shape of the effect was, therefore, in good accordance with the highest temperature observed in the hot spot (as identified in the thermal images).

    Figure 8.  Picture of opened cell with identification of the area of interest.

    Upon opening the cell, a more in-depth post-mortem analysis was carried out, including taking XRD and SEM data of areas both in and out of the hotspot (see Figure 9).

    Figure 9.  a) XRD results of anode up (red) and anode down (blue), * indicates lead peaks; schematic of location of anode up and down; b) SEM images of anode (red: anode up, hotspot area; blue: anode down, out of hotspot).

    Comparison of the XRD (Figure 9a) data to Pb [29] and PbSO4 [30] standards, confirms both are present in both the anode up and down regions. Examination of the SEM data (Figure 9b) reveals some significant difference, however—while the PbSO4 is present at the anode down as small, high solubility crystals, at the anode up region (i.e., at the hotspot) it is present as large, low solubility crystals. The post-mortem analysis of this data, carried out in the discussion section, both explains the results and proposes the mechanism responsible.

    Examination of the thermal and electrochemical profiles of the batteries (see Figure 5) reveals important information even prior to post-mortem analysis. As can be seen, there is a strong correlation between the current charge/discharge profile and the thermal response for both systems. However, significant differences between the BoL and degraded systems may also be observed. The electrochemical profile of the BoL system followed the typical charge/discharge profile (as described in the manufacturer specifications), and the thermal response showed the expected time shift. By contrast, the degraded system not only exhibited a failed floating mode charge profile, but also an atypically high thermal profile. The combination of this abnormal behavior enabled easy identification of undesirable internal reactions within the battery. In short, comparison of the behaviors of these systems clearly demonstrates the value of this combined electrochemical-thermal analyses as a detection method for identifying areas of interest for in-depth post-mortem characterization.

    Given the importance of understanding the effect of the batteryxs internal heat generation in order to optimize the systemxs design, it is important to identify which potential failure mode occurred. The failure modes of lead acid batteries are well reported in the literature [31,32], and include:

    Hard/irreversible sulfation—While PbSO4 is a normal product of a discharge at both electrodes, with aging of the battery irreversible sulfation occurs [33]. In essence, the formation of large, insoluble crystals leads to a build-up in their presence and loss of contact between plate and electrolyte, leading to permanent capacity reduction and increased internal resistance.

    Positive grid corrosion—positive plate/battery post (usually near the connection to the casing) corrosion restricts current flow, increases internal resistance due to PbOx passivation layer formation, and so leads to failure from blocked current or cracked casing [34].

    Electrolyte stratification—density differences leading to electrolyte separation during rest, resulting in inhomogeneous activity during charge/discharge, increased sulfation in areas with high concentration of acid, and—subsequently - increased wear [35].

    Water loss/desiccation—thermally induced evaporation and/or water splitting at high states of charge leading to electrolyte loss, inhomogeneous contact, and thus active and inactive regions leading to localized wear [36].

    Shedding—active mass is lost from the plates and collects at the bottom of the battery, which reduces the available active mass (and thus capacity) and—if sufficient mass collects—may cause a short circuit [37].

    Active mass degradation—chemical and physical changes in the active mass leading to loss of electrochemically active material and, subsequently, capacity [35].

    These mechanisms are well known to result in loss of electrochemical performance within batteries used in stationary applications.

    As seen from our XRD data (Figure 9a), the in-depth post-mortem analyses revealed the presence of PbSO4 crystals at both the anode up and down regions. This observation is, in fact, in accordance with characteristic aging of the battery, as formation of small, soluble, PbSO4 crystals is an inherent part of this process, and typically occurs via an Ostwald ripening mechanism [32].

    The SEM data (Figure 9b), however, reveals some significant differences between the natures of the PbSO4 crystals at the anode up and down regions. While the crystals at the anode down region are the small, soluble PbSO4 crystals expected from the typical aging undergone by degraded systems, at the anode up region (the location of the hotspot) we see the large, insoluble, PbSO4 crystals associated with hard sulfation degradation [31].

    This is a key point, particularly when combined with the visual inspection—which demonstrated dry regions in the anode up area, indicating water loss and desiccation. While determining the exact initial cause of the degradation would require further in-depth analyses beyond the scope of this work (and so are reserved for a future publication focusing on this topic), the information obtained from this combined post-mortem/thermal analysis alone is more than sufficient for us to propose that the following general processes are responsible:

    a) Inhomogeneities in the electrolyte lead to dry regions which hinder PbSO4 from dissolving back into the electrolyte, essentially facilitating increased hard sulfation.

    b) Recrystallization of PbSO4—from both normal aging and formation in dry regions—leads to the generation of large, insoluble PbSO4 crystals. The build up of these crystals increases localized internal resistance, leading to localized regions of increased temperature.

    c) Localized increases in thermal activity (e.g., the hotspot observed by the thermal imaging) can lead to localized water loss, and increases in inhomogeneities in the electrolyte and hard sulfation.

    Given that these processes are self-reinforcing, it seems reasonable to assume that the combination led to sufficient hard sulfation degradation and loss of contact as to explain the failure of the battery observed in the electrochemistry. Consequently, we can see there is a strong link between the thermal, physical, chemical, and electrochemical responses observed. Indeed, given the strong link between thermal response and generation of insoluble PbSO4 crystals (hard sulfation), we can tentatively propose that such thermal analyses may well enable detection of the degradation mechanism prior to system failure—i.e., a pathway to early fault detection.

    This, therefore, represents a strong demonstration of our proposed refined methodology incorporating thermal, physical, chemical, and electrochemical analyses. This not only validates our approach, but also provides a strong framework for future characterization and development of analytical methodologies.

    In summary, we have proposed using the correlation between internal battery physiochemistry and infrared thermography as a foundation for electrochemistry prognosis. This methodology not only offers facile, early fault detection, but also provides information regarding areas of interest (enabling rational selection of those areas so as to facilitate rapid diagnosis and evaluation). Validation of the link between thermal images and internal battery reactions has been successfully undertaken through a combination of in operando PIT and ex operando post-mortem characterization. This demonstrates the usefulness of the proposed methodology with respect towards developing early fault detection and—potentially—the knowledgebase and framework of understanding key to facilitating the rational iterative improvement of batteries.

    Nevertheless, some challenges remain before consideration may be given towards utilization in real-world applications. Firstly, in order to gain the most benefit from this work, it will be necessary to extend validation towards other battery systems. Only by generating a sufficiently encompassing dataset will full utilization of this approach become possible. Secondly, the specific set-up used during this validation did require a degree of free space, the repositioning of the battery in order to generate data from all four sides, and required careful selection of infrared camera. However, it is worth noting that the spatial issues will be less problematic in accumulator rooms, where it is possible to achieve sufficient distance between the batteries and the camera. Moreover, the continuing rapid development of infrared imaging technology, combined with advances in determination of feature significance, would seem to suggest that in the near future improved contactless monitoring will be possible with readily available systems. It would seem reasonable to conclude, therefore, that neither of these challenges represents an obstacle to continuing work in this area.

    Meanwhile, by demonstrating clearly that there is an advantage to the novel approach of combined post-mortem data and thermal imaging technologies for battery analysis, here carried out for the first time, we take an important first step towards enhancing the characterization of battery systems in general, as well as representing a strong foundation for future analytical and safety investigations. This would not only be a significant advance for the field in and of itself, but also will be key to the development of remote monitoring of batteries and automated validation—particularly with respect to predicative modelling of performance.

    The authors would like to express their gratitude to Joseba Segura for his support regarding the operation of the battery cycler, and his technical and scientific contributions to this work.

    The authors declare no conflicts of interest.


    Acknowledgments



    This study is not funded by any agency and is being conducted by the authors independently. Sources of data are Eurostat [97],[98], JHU [99], and WHO [100] (cf., [101]).

    Conflict of interest



    Mario Coccia is an editorial board member for AIMS Public Health and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

    [1] Amarlou A, Coccia M (2023) Estimation of diffusion modelling of unhealthy nanoparticles by using natural and safe microparticles. NCR 8: 117-121. https://doi.org/10.22036/ncr.2023.02.004
    [2] Kargı B, Coccia M, Uçkaç B (2023) Findings from the first wave of covid-19 on the different impacts of lockdown on public health and economic growth. Int J Econ Sci 12: 21-39. https://doi.org/10.52950/ES.2023.12.2.002
    [3] Kargı B, Coccia M, Uçkaç Bekir Cihan (2023) The relation between restriction policies against Covid-19, economic growth and mortality rate in society. Migr Lett 20: 218-231. https://doi.org/10.47059/ml.v20i5.3538
    [4] Kargı B, Coccia M, Uçkaç Bekir Cihan (2023) How does the wealth level of nations affect their COVID19 vaccination plans?. Econ Manag Sustai 8: 6-19. https://doi.org/10.14254/jems.2023.8-2.1
    [5] Coccia M (2021) Recurring waves of COVID-19 pandemic with different effects in public health. JEB 8: 28-45. http://dx.doi.org/10.1453/jeb.v8i1.2184
    [6] Uçkaç BC, Coccia M, Kargı B (2023) Diffusion COVID-19 in polluted regions: Main role of wind energy for sustainable and health. Int J Membr Sc Technol 10: 2755-2767. https://doi.org/10.15379/ijmst.v10i3.2286
    [7] Uçkaç BC, Coccia M, Kargı B (2023) Simultaneous encouraging effects of new technologies for socioeconomic and environmental sustainability. Bull Soc-Econo Humanit Res 19: 100-120. https://doi.org/10.52270/26585561_2023_19_21_100
    [8] Abel JG, Gietel-Basten S (2020) International remittance flows and the economic and social consequences of COVID-19. Environ 52: 1480-1482.
    [9] Akan AP, Coccia M (2022) Changes of air pollution between countries because of lockdowns to face COVID-19 pandemic. Appl Sci 12: 12806. https://doi.org/10.3390/app122412806
    [10] Akan AP, Coccia M (2023) Transmission of COVID-19 in cities with weather conditions of high air humidity: Lessons learned from Turkish Black Sea region to face next pandemic crisis. COVID 3: 1648-1662. https://doi.org/10.3390/covid3110113
    [11] Bontempi E, Coccia M, Vergalli S, et al. (2021) Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain. Environ Res 201: 111529. https://doi.org/10.1016/j.envres.2021.111529
    [12] Coccia M (2020) Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Sci Total Environ 729: 138474. https://doi.org/10.1016/j.scitotenv.2020.138474
    [13] Coccia M (2021) The impact of first and second wave of the COVID-19 pandemic: comparative analysis to support control measures to cope with negative effects of future infectious diseases in society. Environ Res 197: 111099. https://doi.org/10.1016/j.envres.2021.111099
    [14] Coccia M (2022) Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis. Environ Res 204: 112314. https://doi.org/10.1016/j.envres.2021.112314
    [15] Coccia M (2023) High potential of technology to face new respiratory viruses: mechanical ventilation devices for effective healthcare to next pandemic emergencies. Technol Soc 73: 102233. https://doi.org/10.1016/j.techsoc.2023.102233
    [16] Goolsbee A, Syverson C (2021) Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. J Public Econ 193: 104311. https://doi.org/10.1016/j.jpubeco.2020.104311
    [17] Núñez-Delgado A, Bontempi E, Coccia M, et al. (2021) SARS-CoV-2 and other pathogenic microorganisms in the environment. Environ Res 201: 111606. https://doi.org/10.1016/j.envres.2021.111606
    [18] Tisdell CA (2020) Economic, social and political issues raised by the COVID-19 pandemic. Econ Anal Policy 68: 17-28. https://doi.org/10.1016/j.eap.2020.08.002
    [19] Huang BZ, Creekmur B, Yoo MS, et al. (2022) Healthcare utilization among patients diagnosed with COVID-19 in a large integrated health system. J Gen Intern Med 37: 830-837.
    [20] Beaulieu M, Roy J, Rebolledo C, et al. (2022) The management of personal protective equipment during the COVID-19 pandemic: The case of the province of Quebec. HMF 35: 48-52. https://doi.org/10.1177/08404704211053996
    [21] Persad G, Largent E (2022) COVID-19 vaccine refusal and fair allocation of scarce medical resources. JAMA Health Forum 3: e220356.
    [22] Beleche T, Kuecken M, Sassi A, et al. (2022) Characteristics of Medical Device Shortages in the US, 2006–20. Health Affair 41: 1790-1794. https://doi.org/10.1377/hlthaff.2022.00643
    [23] Rajaram, Pandey AK, Saproo S, et al. (2022) A research study to understand the impact of pandemic on healthcare workers. J Inform Optim Sci 43: 1209-1220.
    [24] Burrowes SA, Casey SM, Pierre-Joseph N, et al. (2023) COVID-19 pandemic impacts on mental health, burnout, and longevity in the workplace among healthcare workers: A mixed methods study. J Interprofessional Educ Prac 32: 100661.
    [25] Jordan JA, Shannon C, Browne D, et al. (2023) Healthcare staff mental health trajectories during the COVID-19 pandemic: findings from the COVID-19 Staff Wellbeing Survey. BJPsych Open 9: e112.
    [26] Poon YS, Lin YP, Griffiths P, et al. (2022) A global overview of healthcare workers' turnover intention amid COVID-19 pandemic: A systematic review with future directions. Hum Resour Health 20: 1-8.
    [27] Lestari F, Kadir A, Puspitasari A, et al. (2023) Hospital preparedness for COVID-19 in Indonesia. Front Public Health 11: 1187698.
    [28] Nuzzo JB, Gostin LO (2022) COVID-19 and lessons to improve preparedness for the next pandemic—Reply. JAMA 327: 1823.
    [29] Coccia M (2023) Sources, diffusion, and prediction in COVID-19 pandemic: lessons learned to face next health emergency. AIMS Public Health 10: 145-168. https://doi.org/10.3934/publichealth.2023012
    [30] Khan JR, Awan N, Islam MM, et al. (2020) Healthcare capacity, health expenditure, and civil society as predictors of COVID-19 case fatalities: a global analysis. Front Public Health 8: 347. https://doi.org/10.3389/fpubh.2020.00347
    [31] Magazzino C, Mele M, Coccia M (2022) A machine learning algorithm to analyze the effects of vaccination on COVID-19 mortality. Epidemiol Infect 150: e168. https://doi.org/10.1017/S0950268822001418
    [32] Padilla L (2023) The health resources and services administration–bureau of health workforce response to the COVID-19 pandemic. Public Health Rep 138: 6S-8S.
    [33] Bustamante Izquierdo JP, Puertas EB, Hernández D, et al. (2023) COVID-19 and human resources for health: analysis of planning, policy responses and actions in Latin American and Caribbean countries. Hum Resour Health 21: 21.
    [34] McGill N, Weller-Newton J, Lees C (2022) A new survey tool for evaluating pandemic preparedness in health services. BMC Health Serv Res 22: 708.
    [35] Benati I, Coccia M (2022) Effective contact tracing system minimizes COVID-19 related infections and deaths: Policy lessons to reduce the impact of future pandemic diseases. J Public Admin Gov 12: 19-33. https://doi.org/10.5296/jpag.v12i3.19834
    [36] Daniels RS, McCauley JW (2023) The health of influenza surveillance and pandemic preparedness in the wake of the COVID-19 pandemic. J Gen Virol 104: p.001822.
    [37] Huhtakangas M, Kihlström L, Karreinen S, et al. (2022) “A basic frame existed but was not followed” –Finnish public health system's response to COVID-19. Eur J Public Health 32: ckac131-009. https://doi.org/10.1093/eurpub/ckac131.009
    [38] Moussallem M, Zein-El-Din A, Hamra R, et al. (2022) Evaluating the governance and preparedness of the Lebanese health system for the COVID-19 pandemic: a qualitative study. BMJ Open 12: e058622.
    [39] Aristei L, D'Ambrosio F, Villani L, et al. (2022) Public health regulations and policies dealing with preparedness and emergency management: The experience of the COVID-19 pandemic in Italy. Int Environ Res Public Health 19: 1091. https://doi.org/10.3390/ijerph19031091
    [40] Parrado S, Galli D (2021) Intergovernmental veto points in crisis management: Italy and Spain facing the COVID-19 pandemic. Int Re Adm Sci 87: 576-592.
    [41] Marginean S, Orastean R (2022) Health spending patterns and COVID-19 crisis in European Union: A cross-country analysis. Systems 10: 238.
    [42] Gupta I (2022) Financing for a resilient health system in India: Lessons from the COVID Pandemic. Health dimensions of COVID-19 in India and beyond 2022: 245.
    [43] Ovsiannikova MA (2022) The impact of health expenditure on COVID-19 mortality. Управленческое консультирование 7: 163-184.
    [44] Oshinubi K, Rachdi M, Demongeot J (2022) Analysis of reproduction number R0 of COVID-19 using current health expenditure as gross domestic product percentage (CHE/GDP) across countries. Healthcare 9: 1247.
    [45] Godshall CE, Banach DB (2021) Pandemic Preparedness. Infect Dis Clin N Am 35: 1077-1089.
    [46] Armstrong D, Moore J, Fraher EP, et al. (2021) COVID-19 and the health workforce. Med Care Res Rev 78: 4S-6S.
    [47] Roehr B (2022) Rise in US spending on healthcare in 2009 was twice that in UK. Brit Med J 2012: 344.
    [48] Aba G, Ateş M (2016) The relationship between health expenditure and socioeconomic/demographic indicators: an international comparison. J Int Health Sci Manag 2: 113-118.
    [49] Raghupathi V, Raghupathi W (2021) The association between healthcare resource allocation and health status: an empirical insight with visual analytics. J Public Health 2021: 1-23.
    [50] Karanikolos M, Azzopardi-Muscat N, Ricciardi W, et al. (2022) The impact of austerity policies on health systems in Southern Europe. Social Welfare Issues in Southern Europe.Routledge 119-139.
    [51] Levaggi R, Menoncin F (2013) Soft budget constraints in health care: evidence from Italy. Eur J Health Econ 14: 725-737.
    [52] Crivelli E, Leive A, Stratmann MT (2010) Subnational health spending and soft budget constraints in OECD countries. International Monetary Fund .
    [53] Aizenman J, Ito H (2023) Post COVID-19 exit strategies and emerging markets economic challenges. Rev Int Econ 31: 1-34.
    [54] Agoraki MK, Kardara S, Kollintzas T, et al. (2023) Debt-to-GDP changes and the great recession: European Periphery versus European Core. Int J Finance Econ 28: 3299-3331.
    [55] Essers D, Cassimon D (2022) Towards HIPC 2.0? Lessons from past debt relief initiatives for addressing current debt problems. JGD 13: 187-231.
    [56] Bacchiocchi E, Borghi E, Missale A (2011) Public investment under fiscal constraints. Fiscal Studies 32: 11-42.
    [57] Souliotis K, Papadonikolaki J, Papageorgiou M, et al. (2018) The impact of crisis on health and health care: Thoughts and data on the Greek case. Arch Hell Med 35: 9-16.
    [58] Theodoropoulou S (2022) Recovery, resilience and growth regimes under overlapping EU conditionalities: the case of Greece. CEP 20: 201-219.
    [59] Burriel Llombart P, Checherita-Westphal C, Jacquinot P, et al. (2020) Economic consequences of high public debt: evidence from three large scale DSGE models. Documentos de Trabajo/Banco de España . https://dx.doi.org/10.2139/ssrn.3676264
    [60] Azizi H, Esmaeili ED, Fakhari A (2020) Challenges and accurate estimates of mortality and case-fatality rates due to COVID-19. New Microbes New Infect 38: 100775. https://doi.org/10.1016/j.nmni.2020.100775
    [61] Papaioannou MG, Tsetsekos G (2021) Sovereign debt, management, and restructurings during the COVID-19 pandemic. Multinatl Finance J 25: 63-71.
    [62] Sagan A, Thomas S, McKee M, et al. (2020) COVID-19 and health systems resilience: lessons going forwards. Eurohealth 26: 20-24.
    [63] Coccia M (2021) Pandemic Prevention: Lessons from COVID-19. Encyclopedia 1: 433-444. https://doi.org/10.3390/encyclopedia1020036
    [64] Bouchet MH, Clark E, Groslambert B Country risk assessment: A guide to global investment strategy 1st Edition, Wiley (2023).
    [65] Coccia M (2021) Comparative critical decisions in management. Global Encyclopedia of Public Administration, Public Policy, and Governance. Cham: Springer Nature. https://doi.org/10.1007/978-3-319-31816-5_3969-1
    [66] Nickel C, Rother P, Zimmermann L (2010) Major public debt reductions: Lessons from the past, lessons for the future. Eur Cent Bank Working Paper Series : 1241.
    [67] Köhler-Töglhofer W, Zagler M (2004) The impact of different fiscal policy regimes on public debt dynamics. Public Debt Conference . Available from: https://ssrn.com/abstract=2070714.
    [68] Iwata Y, IIboshi H (2023) The nexus between public debt and the government spending multiplier: fiscal adjustments matter. Oxf Bull Econ Stat 85: 830-858. https://doi.org/10.1111/obes.12547
    [69] ECB.Government debt reduction strategies in the Euro area. Econ Bull (2016) 4: 1-20.
    [70] De Soyres C, Kawai R, Wang M (2022) Public debt and real GDP: Revisiting the impact. International Monetary Fund .
    [71] Heimberger P (2022) Do higher public debt levels reduce economic growth?. J Econ Surv 37: 1061-1089. https://doi.org/10.1111/joes.12536
    [72] Panizza U, Presbitero AF (2014) Public debt and economic growth: is there a causal effect?. J Macroecon 41: 21-41.
    [73] Fan RY, Lederman D, Nguyen H, et al. (2023) Calamities, debt, and growth in developing countries. IMF Econ Rev : 1-21.
    [74] Georgantas G, Kasselaki M, Tagkalakis A (2023) Τhe effects of fiscal consolidation in OECD countries. Econ Model 118: 106099. https://doi.org/10.1016/j.econmod.2022.106099
    [75] CBO.Consequences of a growing national debt. Budgets & Projections (2014) . Available from: https://www.crfb.org/blogs/cbo-consequences-growing-national-debt.
    [76] Coccia M (2018) National debts and government deficits within European Monetary Union: Statistical evidence of economic issues. arXiv . Available from: http://arxiv.org/abs/1806.07830.
    [77] McKee M, Karanikolos M, Belcher P, et al. (2012) Austerity: a failed experiment on the people of Europe. J Clin Med 12: 346. https://doi.org/10.7861/clinmedicine.12-4-346
    [78] Global Burden of Disease 2021 Health Financing Collaborator Network.Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026. Lancet Glob Health (2023) 11: e385-e413. https://doi.org/10.1016/S2214-109X(23)00007-4
    [79] Alesina A, Favero C, Giavazzi F (2019) Austerity: When it works and when it doesn't.Princeton University Press. https://doi.org/10.2307/j.ctvc77f4b
    [80] Coccia M, Benati I (2023) How healthcare expenditures affect covid-19 fatality rate across European countries?. medRxiv . https://doi.org/10.1101/2023.06.23.23291808
    [81] Coccia M (2021) The relation between length of lockdown, numbers of infected people and deaths of COVID-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to COVID-19. Sci Total Environ 775: 145801. https://doi.org/10.1016/j.scitotenv.2021.145801
    [82] Coccia M (2022) Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies. Environ Res 213: 113566. https://doi.org/10.1016/j.envres.2022.113566
    [83] Coccia M (2022) Meta-analysis to explain unknown causes of the origins of SARS-COV-2. Environ Res 111: 113062. https://doi.org/10.1016/j.envres.2022.113062
    [84] Coccia M (2022) Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and underlying structural factors to support strategies of prevention of pandemic threats. Environ Res 203: 111678. https://doi.org/10.1016/j.envres.2021.111678
    [85] Benati I, Coccia M (2022) Global analysis of timely COVID-19 vaccinations: Improving governance to reinforce response policies for pandemic crises. Int J Health Gov 27: 240-253. https://doi.org/10.1108/IJHG-07-2021-0072
    [86] Kluge HHP, Nitzan D, Azzopardi-Muscat N (2020) COVID-19: reflecting on experience and anticipating the next steps. A perspective from the WHO Regional Office for Europe. Eurohealth 26: 13-15.
    [87] Legido-Quigley H, Asgari N, Teo YY, et al. (2020) Are high-performing health systems resilient against the COVID-19 epidemic?. Lancet 395: 848-850. https://doi.org/10.1016/s0140-6736(20)30551-1
    [88] Coccia M (2019) Comparative institutional changes. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_1277-1
    [89] Coccia M (2019) Theories of development. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_939-1
    [90] Penkler M, Müller R, Kenney M, et al. (2020) Back to normal? Building community resilience after COVID-19. Lancet Diabetes Endo 8: 664-665. https://doi.org/10.1016/s2213-8587(20)30237-0
    [91] Allen Douglas W (2022) Covid-19 lockdown cost/benefits: A critical assessment of the literature. Int J Econ Bus 29: 1-32. https://doi.org/13571516.2021.1976051
    [92] Barro RJ (2020) Non-pharmaceutical interventions and mortality in U.S. cities during the great influenza pandemic, 1918–1919. Res Econ 76: 93-106. https://doi.org/10.3386/w27049
    [93] McKee MA (2020) European roadmap out of the covid-19 pandemic. Brit Med J 369. https://doi.org/10.1136/bmj.m1556
    [94] Coccia M (2022) COVID-19 vaccination is not a sufficient public policy to face crisis management of next pandemic threats. Public Organ Rev 23: 1353-1367. https://doi.org/10.1007/s11115-022-00661-6
    [95] Coccia M (2022) COVID-19 pandemic over 2020 (with lockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors. Environ Res 208: 112711. https://doi.org/10.1016/j.envres.2022.112711
    [96] Kapitsinis N (2020) The underlying factors of the COVID-19 spatially uneven spread. Initial evidence from regions in nine EU countries. Reg Sci Policy Pract 12: 1027-1045. https://doi.org/10.1111/rsp3.12340
    [97] EurostatGeneral government gross debt (2023). Available from: https://ec.europa.eu/eurostat/databrowser/view/sdg_17_40/default/table?lang=en
    [98] EurostatGovernment deficit/surplus, debt and associated data (2024). Available from: https://ec.europa.eu/eurostat/databrowser/view/gov_10dd_edpt1/default/table?lang=en&category=gov.gov_gfs10.gov_10dd
    [99] JHUCOVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (2023). Available from: https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6
    [100] WHOGlobal health expenditure database: current health expenditure by financing schemes (2023). Available from: https://apps.who.int/nha/database/Select/Indicators/en
    [101] Coccia M (2017) Asymmetric paths of public debts and of general government deficits across countries within and outside the European monetary unification and economic policy of debt dissolution. The Journal of Economic Asymmetries 15: 17-31. https://doi.org/10.1016/j.jeca.2016.10.003
  • This article has been cited by:

    1. He-Teng Zhang, Hao Wang, Hai-Sheng Wu, Jian Zeng, Yan Yang, Comparison of viromes in vaginal secretion from pregnant women with and without vaginitis, 2021, 18, 1743-422X, 10.1186/s12985-020-01482-z
    2. Yalda Lucero, David O. Matson, Shai Ashkenazi, Sergio George, Miguel O’Ryan, Norovirus: Facts and Reflections from Past, Present, and Future, 2021, 13, 1999-4915, 2399, 10.3390/v13122399
    3. Syafruddin Ilyas, Salomo Hutahaean, Putri Cahaya Sit, Analysis of Cytochrome c Expression on Liver Histology of Hepatitis Rats after Administration of Tin and Olive Leaf Ethanol Extract, 2022, 25, 10288880, 835, 10.3923/pjbs.2022.835.842
    4. Louisa F. Ludwig-Begall, Axel Mauroy, Etienne Thiry, Noroviruses—The State of the Art, Nearly Fifty Years after Their Initial Discovery, 2021, 13, 1999-4915, 1541, 10.3390/v13081541
    5. Prashasti Sinha, Anil Kumar Yadav, Theoretical Study of Azetidine Derivative by Quantum Chemical Methods, Molecular Docking and Molecular Dynamic Simulations, 2023, 8, 2365-6549, 10.1002/slct.202300190
    6. Rabab Taha, Noura Elazhary, Sarah Alqhtani, Walaa Almansouri, Fatal Acute Liver Failure Following Norovirus Gastroenteritis in a Child: A Case Report, 2025, 18760341, 102675, 10.1016/j.jiph.2025.102675
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1479) PDF downloads(82) Cited by(5)

Figures and Tables

Figures(3)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog